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Butler K, Ahmed S, Jablonski J, Hookway TA. Engineered Cardiac Microtissue Biomanufacturing Using Human Induced Pluripotent Stem Cell Derived Epicardial Cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.13.593960. [PMID: 38798424 PMCID: PMC11118268 DOI: 10.1101/2024.05.13.593960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Epicardial cells are a crucial component in constructing in vitro 3D tissue models of the human heart, contributing to the ECM environment and the resident mesenchymal cell population. Studying the human epicardium and its development from the proepicardial organ is difficult, but induced pluripotent stem cells can provide a source of human epicardial cells for developmental modeling and for biomanufacturing heterotypic cardiac tissues. This study shows that a robust population of epicardial cells (approx. 87.7% WT1+) can be obtained by small molecule modulation of the Wnt signaling pathway. The population maintains WT1 expression and characteristic epithelial morphology over successive passaging, but increases in size and decreases in cell number, suggesting a limit to their expandability in vitro. Further, low passage number epicardial cells formed into more robust 3D microtissues compared to their higher passage counterparts, suggesting that the ideal time frame for use of these epicardial cells for tissue engineering and modeling purposes is early on in their differentiated state. Additionally, the differentiated epicardial cells displayed two distinct morphologic sub populations with a subset of larger, more migratory cells which led expansion of the epicardial cells across various extracellular matrix environments. When incorporated into a mixed 3D co-culture with cardiomyocytes, epicardial cells promoted greater remodeling and migration without impairing cardiomyocyte function. This study provides an important characterization of stem cell-derived epicardial cells, identifying key characteristics that influence their ability to fabricate consistent engineered cardiac tissues.
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Affiliation(s)
- Kirk Butler
- Biomedical Engineering Department, Binghamton University, the State University of New York, Binghamton NY 13902
| | - Saif Ahmed
- Biomedical Engineering Department, Binghamton University, the State University of New York, Binghamton NY 13902
| | - Justin Jablonski
- Biomedical Engineering Department, University of Rochester, Rochester, NY14627
| | - Tracy A. Hookway
- Biomedical Engineering Department, Binghamton University, the State University of New York, Binghamton NY 13902
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2
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Zhang R, Anguiano M, Aarrestad IK, Lin S, Chandra J, Vadde SS, Olson DE, Kim CK. Rapid, biochemical tagging of cellular activity history in vivo. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.06.556431. [PMID: 38798353 PMCID: PMC11118534 DOI: 10.1101/2023.09.06.556431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Intracellular calcium (Ca2+) is ubiquitous to cell signaling across all biology. While existing fluorescent sensors and reporters can detect activated cells with elevated Ca2+ levels, these approaches require implants to deliver light to deep tissue, precluding their noninvasive use in freely-behaving animals. Here we engineered an enzyme-catalyzed approach that rapidly and biochemically tags cells with elevated Ca2+ in vivo. Ca2+-activated Split-TurboID (CaST) labels activated cells within 10 minutes with an exogenously-delivered biotin molecule. The enzymatic signal increases with Ca2+ concentration and biotin labeling time, demonstrating that CaST is a time-gated integrator of total Ca2+ activity. Furthermore, the CaST read-out can be performed immediately after activity labeling, in contrast to transcriptional reporters that require hours to produce signal. These capabilities allowed us to apply CaST to tag prefrontal cortex neurons activated by psilocybin, and to correlate the CaST signal with psilocybin-induced head-twitch responses in untethered mice.
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Affiliation(s)
- Run Zhang
- Biomedical Engineering Graduate Group, University of California, Davis, Davis, CA 95616
- Center for Neuroscience, University of California, Davis, Davis, CA 95618
| | - Maribel Anguiano
- Center for Neuroscience, University of California, Davis, Davis, CA 95618
- Neuroscience Graduate Group, University of California, Davis, Davis, CA 95618
| | - Isak K. Aarrestad
- Center for Neuroscience, University of California, Davis, Davis, CA 95618
- Neuroscience Graduate Group, University of California, Davis, Davis, CA 95618
- Institute for Psychedelics and Neurotherapeutics, University of California, Davis, Davis, CA 95616
| | - Sophia Lin
- Center for Neuroscience, University of California, Davis, Davis, CA 95618
- Department of Neurology, University of California, Davis, Sacramento, CA 95817
| | - Joshua Chandra
- Center for Neuroscience, University of California, Davis, Davis, CA 95618
- Neuroscience Graduate Group, University of California, Davis, Davis, CA 95618
| | - Sruti S. Vadde
- Center for Neuroscience, University of California, Davis, Davis, CA 95618
- Department of Neurology, University of California, Davis, Sacramento, CA 95817
| | - David E. Olson
- Center for Neuroscience, University of California, Davis, Davis, CA 95618
- Institute for Psychedelics and Neurotherapeutics, University of California, Davis, Davis, CA 95616
- Department of Chemistry, University of California, Davis, Davis, CA 95616
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Sacramento, CA 95817
| | - Christina K. Kim
- Center for Neuroscience, University of California, Davis, Davis, CA 95618
- Institute for Psychedelics and Neurotherapeutics, University of California, Davis, Davis, CA 95616
- Department of Neurology, University of California, Davis, Sacramento, CA 95817
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3
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Chai Y, Qi K, Wu Y, Li D, Tan G, Guo Y, Chu J, Mu Y, Shen C, Wen Q. All-optical interrogation of brain-wide activity in freely swimming larval zebrafish. iScience 2024; 27:108385. [PMID: 38205255 PMCID: PMC10776927 DOI: 10.1016/j.isci.2023.108385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 09/22/2023] [Accepted: 10/30/2023] [Indexed: 01/12/2024] Open
Abstract
We introduce an all-optical technique that enables volumetric imaging of brain-wide calcium activity and targeted optogenetic stimulation of specific brain regions in unrestrained larval zebrafish. The system consists of three main components: a 3D tracking module, a dual-color fluorescence imaging module, and a real-time activity manipulation module. Our approach uses a sensitive genetically encoded calcium indicator in combination with a long Stokes shift red fluorescence protein as a reference channel, allowing the extraction of Ca2+ activity from signals contaminated by motion artifacts. The method also incorporates rapid 3D image reconstruction and registration, facilitating real-time selective optogenetic stimulation of different regions of the brain. By demonstrating that selective light activation of the midbrain regions in larval zebrafish could reliably trigger biased turning behavior and changes of brain-wide neural activity, we present a valuable tool for investigating the causal relationship between distributed neural circuit dynamics and naturalistic behavior.
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Affiliation(s)
- Yuming Chai
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Research Center for Physical Sciences at the Microscale, Center for Integrative Imaging, University of Science and Technology of China, Hefei, China
| | - Kexin Qi
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Research Center for Physical Sciences at the Microscale, Center for Integrative Imaging, University of Science and Technology of China, Hefei, China
| | - Yubin Wu
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Research Center for Physical Sciences at the Microscale, Center for Integrative Imaging, University of Science and Technology of China, Hefei, China
| | - Daguang Li
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Research Center for Physical Sciences at the Microscale, Center for Integrative Imaging, University of Science and Technology of China, Hefei, China
| | - Guodong Tan
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Research Center for Physical Sciences at the Microscale, Center for Integrative Imaging, University of Science and Technology of China, Hefei, China
| | - Yuqi Guo
- Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology and Center for Biomedical Optics and Molecular Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jun Chu
- Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology and Center for Biomedical Optics and Molecular Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yu Mu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Chen Shen
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Research Center for Physical Sciences at the Microscale, Center for Integrative Imaging, University of Science and Technology of China, Hefei, China
| | - Quan Wen
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Research Center for Physical Sciences at the Microscale, Center for Integrative Imaging, University of Science and Technology of China, Hefei, China
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4
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Pajanoja C, Kerosuo L. ShapeMetrics: A 3D Cell Segmentation Pipeline for Single-Cell Spatial Morphometric Analysis. Methods Mol Biol 2024; 2767:263-273. [PMID: 37219813 DOI: 10.1007/7651_2023_489] [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: 05/24/2023]
Abstract
There is a growing need for single-cell level data analysis in correlation with the advancements of microscopy techniques. Morphology-based statistics gathered from individual cells are essential for detection and quantification of even subtle changes within the complex tissues, yet the information available from high-resolution imaging is oftentimes sub-optimally utilized due to the lack of proper computational analysis software. Here we present ShapeMetrics, a 3D cell segmentation pipeline that we have developed to identify, analyze, and quantify single cells in an image. This MATLAB-based script enables users to extract morphological parameters, such as ellipticity, longest axis, cell elongation, or the ratio between cell volume and surface area. We have specifically invested in creating a user-friendly pipeline, aimed for biologists with a limited computational background. Our pipeline is presented with detailed stepwise instructions, starting from the establishment of machine learning-based prediction files of immuno-labeled cell membranes followed by the application of 3D cell segmentation and parameter extraction script, leading to the morphometric analysis and spatial visualization of cell clusters defined by their morphometric features.
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Affiliation(s)
- Ceren Pajanoja
- Neural Crest Development and Disease Unit, National Institute of Dental and Craniofacial Research, Intramural Research Program, Neural Crest Development and Disease Unit, National Institutes of Health, Bethesda, ML, USA
- Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Laura Kerosuo
- Neural Crest Development and Disease Unit, National Institute of Dental and Craniofacial Research, Intramural Research Program, Neural Crest Development and Disease Unit, National Institutes of Health, Bethesda, ML, USA
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5
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Mazur M, Krauze W. Volumetric segmentation of biological cells and subcellular structures for optical diffraction tomography images. BIOMEDICAL OPTICS EXPRESS 2023; 14:5022-5035. [PMID: 37854559 PMCID: PMC10581803 DOI: 10.1364/boe.498275] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/27/2023] [Accepted: 07/31/2023] [Indexed: 10/20/2023]
Abstract
Three-dimensional, quantitative imaging of biological cells and their internal structures performed by optical diffraction tomography (ODT) is an important part of biomedical research. However, conducting quantitative analysis of ODT images requires performing 3D segmentation with high accuracy, often unattainable with available segmentation methods. Therefore, in this work, we present a new semi-automatic method, called ODT-SAS, which combines several non-machine-learning techniques to segment cells and 2 types of their organelles: nucleoli and lipid structures (LS). ODT-SAS has been compared with Cellpose and slice-by-slice manual segmentation, respectively, in cell segmentation and organelles segmentation. The comparison shows superiority of ODT-SAS over Cellpose and reveals the potential of our technique in detecting cells, nucleoli and LS.
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Affiliation(s)
- Martyna Mazur
- Warsaw University of Technology, 8 Boboli Str., Warsaw, 02-525, Poland
| | - Wojciech Krauze
- Warsaw University of Technology, 8 Boboli Str., Warsaw, 02-525, Poland
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6
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Franceschini A, Mazzamuto G, Checcucci C, Chicchi L, Fanelli D, Costantini I, Passani MB, Silva BA, Pavone FS, Silvestri L. Brain-wide neuron quantification toolkit reveals strong sexual dimorphism in the evolution of fear memory. Cell Rep 2023; 42:112908. [PMID: 37516963 DOI: 10.1016/j.celrep.2023.112908] [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: 02/14/2023] [Revised: 06/07/2023] [Accepted: 07/14/2023] [Indexed: 08/01/2023] Open
Abstract
Fear responses are functionally adaptive behaviors that are strengthened as memories. Indeed, detailed knowledge of the neural circuitry modulating fear memory could be the turning point for the comprehension of this emotion and its pathological states. A comprehensive understanding of the circuits mediating memory encoding, consolidation, and retrieval presents the fundamental technological challenge of analyzing activity in the entire brain with single-neuron resolution. In this context, we develop the brain-wide neuron quantification toolkit (BRANT) for mapping whole-brain neuronal activation at micron-scale resolution, combining tissue clearing, high-resolution light-sheet microscopy, and automated image analysis. The robustness and scalability of this method allow us to quantify the evolution of activity patterns across multiple phases of memory in mice. This approach highlights a strong sexual dimorphism in recruited circuits, which has no counterpart in the behavior. The methodology presented here paves the way for a comprehensive characterization of the evolution of fear memory.
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Affiliation(s)
- Alessandra Franceschini
- European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy; Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy.
| | - Giacomo Mazzamuto
- European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy; Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy; National Institute of Optics - National Research Council (CNR-INO), Sesto Fiorentino, Italy
| | - Curzio Checcucci
- Department of Information Engineering (DINFO), University of Florence, Florence, Italy
| | - Lorenzo Chicchi
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
| | - Duccio Fanelli
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
| | - Irene Costantini
- European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy; Department of Biology, University of Florence, Florence, Italy
| | | | - Bianca Ambrogina Silva
- National Research Council of Italy, Institute of Neuroscience, Milan, Italy; IRCCS Humanitas Research Hospital, Lab of Circuits Neuroscience, Rozzano, Milan, Italy
| | - Francesco Saverio Pavone
- European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy; Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy; National Institute of Optics - National Research Council (CNR-INO), Sesto Fiorentino, Italy
| | - Ludovico Silvestri
- European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy; Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy; National Institute of Optics - National Research Council (CNR-INO), Sesto Fiorentino, Italy.
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7
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Saha D, Dhyani V, Giri L. In vitro laser scanning confocal microscopy and unsupervised segmentation: Quantification of cytosolic calcium and RNA distribution in hypoxic neurons. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083364 DOI: 10.1109/embc40787.2023.10340952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The mimicry of neurodegenerative diseases in vitro can be observed through the induction of chronic hypoxia, and the impact of this stress is monitored using multiplexed imaging techniques. While laser scanning confocal microscopy (LSCM) is a valuable tool for observing single neurons under degenerative conditions, accurately quantifying RNA distribution and cell size by deep learning tools remains challenging due to the lack of annotated training datasets. To address this, we propose a framework that combines 3D tracking of RNA distribution and cell size identification using unsupervised image segmentation. Additionally, we quantified the calcium level in neurons using fluorescent microscopy using unsupervised image segmentation. First, we performed imaging of neuronal morphology using differential interference contrast (DIC) optics and RNA/calcium level imaging using fluorescent microscopy. Next, we performed k-means clustering-based cell segmentation. The results show that our framework can distinguish between distinct neuronal states under control and chronic hypoxic conditions. The analysis reveals that hypoxia induces a significant increase in cytosolic calcium level, reduction in neuron diameter, and alterations in RNA distribution.Clinical Relevance- The proposed framework is crucial to study the neurodegeneration process and evaluating the efficacy of neuroprotective drugs through image analysis.
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8
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Devulapally A, Parekh V, Pazhayidam George C, Balakrishnan S. On the Variability in Cell and Nucleus Shapes. Cells Tissues Organs 2022; 213:96-107. [PMID: 36315993 DOI: 10.1159/000527825] [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: 06/03/2022] [Accepted: 10/26/2022] [Indexed: 02/17/2024] Open
Abstract
Cell morphology is an important regulator of cell function. Many abnormalities in cellular behavior can be discerned from changes in the shape of the cell and its organelles, typically the nucleus. Two major challenges for developing such phenotypic assays are reconstructing 3D surfaces of individual cells and nuclei from confocal images and developing characterizations of these surfaces for comparisons. We demonstrate two algorithms - 3D active contours and 3D condensed-attention UNet - to segment cells and nuclei from confocal images. The cell and nuclear surfaces are then converted into vectors using a reversible, spherical transform - i.e., shapes can be recovered from the vectors. Typical methods for characterizing shapes using size, shape, and image parameters such as area, volume, shape factor, solidity, and pixel intensities are not amenable to such reverse transformation. Our vector representation's principal component analysis shows that the significant modes of variability among cell and nucleus shapes are scaling and flattening. We benchmark these modes using a known mechanical model for nucleus morphology. Subsequent modes alter the eccentricity of the nucleus and translate and rotate it with respect to the cell. Our vector-space representation of cell and nucleus shape helps physically interpret the variability sources. It may further help to guide mechanical models and identify molecular mechanisms driving cell and nuclear shape changes.
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Affiliation(s)
- Anusha Devulapally
- School of Mathematics and Computer Science, Indian Institute of Technology Goa, Veling, India
| | - Varun Parekh
- School of Mathematics and Computer Science, Indian Institute of Technology Goa, Veling, India
| | - Clint Pazhayidam George
- School of Mathematics and Computer Science, Indian Institute of Technology Goa, Veling, India
- School of Interdisciplinary Life Sciences, Indian Institute of Technology Goa, Veling, India
| | - Sreenath Balakrishnan
- School of Interdisciplinary Life Sciences, Indian Institute of Technology Goa, Veling, India
- School of Mechanical Sciences, Indian Institute of Technology Goa, Veling, India
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9
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Mattson CL, Okamura K, Hume PS, Smith BJ. Spatiotemporal distribution of cellular injury and leukocytes during the progression of ventilator-induced lung injury. Am J Physiol Lung Cell Mol Physiol 2022; 323:L281-L296. [PMID: 35700201 PMCID: PMC9423727 DOI: 10.1152/ajplung.00207.2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 05/26/2022] [Accepted: 06/12/2022] [Indexed: 11/22/2022] Open
Abstract
Supportive mechanical ventilation is a necessary lifesaving treatment for acute respiratory distress syndrome (ARDS). This intervention often leads to injury exacerbation by ventilator-induced lung injury (VILI). Patterns of injury in ARDS and VILI are recognized to be heterogeneous; however, quantification of these injury distributions remains incomplete. Developing a more detailed understanding of injury heterogeneity, particularly how it varies in space and time, can help elucidate the mechanisms of VILI pathogenesis. Ultimately, this knowledge can be used to develop protective ventilation strategies that slow disease progression. To expand existing knowledge of VILI heterogeneity, we document the spatial evolution of cellular injury distribution and leukocyte infiltration, on the micro- and macroscales, during protective and injurious mechanical ventilation. We ventilated naïve mice using either high inspiratory pressure and zero positive end-expiratory pressure ventilation or low tidal volume with positive end-expiratory pressure. Distributions of cellular injury, identified with propidium iodide staining, were microscopically analyzed at three levels of injury severity. Cellular injury initiated in diffuse, quasi-random patterns, and progressed through expansion of high-density regions of injured cells termed "injury clusters." The density profile of the expanding injury regions suggests that stress shielding occurs, protecting the already injured regions from further damage. Spatial distribution of leukocytes did not correlate with that of cellular injury or ventilation-induced changes in lung function. These results suggest that protective ventilation protocols should protect the interface between healthy and injured regions to stymie injury propagation.
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Affiliation(s)
- Courtney L Mattson
- Department of Bioengineering, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado
| | - Kayo Okamura
- Department of Bioengineering, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado
| | - Patrick S Hume
- Department of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, Denver, Colorado
- Department of Pediatrics, Pulmonary and Sleep Medicine, School of Medicine, University of Colorado, Aurora, Colorado
| | - Bradford J Smith
- Department of Bioengineering, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado
- Department of Pediatrics, Pulmonary and Sleep Medicine, School of Medicine, University of Colorado, Aurora, Colorado
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10
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Yang L, Brouillette MJ, Coleman MC, Kluz PN, Goetz JE. Automated quantification of live articular chondrocyte fluorescent staining using a custom image analysis framework. J Orthop Res 2022; 40:1203-1212. [PMID: 34191348 DOI: 10.1002/jor.25137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 05/03/2021] [Accepted: 06/09/2021] [Indexed: 02/04/2023]
Abstract
The goal of this study was to develop, validate, and implement an image analysis framework to automatically analyze chondrocytes in 3D image stacks of cartilage acquired using a fluorescent confocal microscope. Source specimens consist of viable osteochondral tissue co-stained with multiple live-cell dyes. Our framework utilizes a seeded watershed-based algorithm to automatically segment individual chondrocytes in each 2D slice of the confocal image stack. The resulting cell segmentations are colocalized in 3D to eliminate duplicate segmentation of the same cell resulting from the visibility of fluorescence signal in multiple imaging planes, and the 3D cell distribution is used to automatically define the cartilage tissue volume. The algorithm then provides chondrocyte density data, and the associated segmentation can be used as a mask to extract and quantify per cell intensity of a secondary, functional dye co-staining the chondrocytes. The accuracy of the automated chondrocyte segmentation was validated against manual segmentations (average IOU = 0.79). When applied to a cartilage surrogate, this analysis framework estimated chondrocyte density within 10% of the true density and demonstrated a good agreement between framework's counts and manual counts (R2 = 0.99). In a real application, the framework was able to detect the increased dye signal of monochlorobimane (MCB) in chondrocytes treated with N-acetylcysteine (NAC) after mechanical injury, quantifying intracellular biochemical changes in living cells. This new framework allows for fast and accurate quantification of intracellular activities of chondrocytes, and it can be adapted for broader application in many imaging and treatment modalities, including therapeutic OA research.
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Affiliation(s)
- Linjun Yang
- Department of Orthopedics and Rehabilitation, University of Iowa, Iowa City, Iowa, USA.,Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
| | - Marc J Brouillette
- Department of Orthopedics and Rehabilitation, University of Iowa, Iowa City, Iowa, USA
| | - Mitchell C Coleman
- Department of Orthopedics and Rehabilitation, University of Iowa, Iowa City, Iowa, USA.,Department of Radiation Oncology, University of Iowa, Iowa City, Iowa, USA
| | - Paige N Kluz
- Department of Orthopedics and Rehabilitation, University of Iowa, Iowa City, Iowa, USA
| | - Jessica E Goetz
- Department of Orthopedics and Rehabilitation, University of Iowa, Iowa City, Iowa, USA.,Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
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11
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Allenby MC, Woodruff MA. Image analyses for engineering advanced tissue biomanufacturing processes. Biomaterials 2022; 284:121514. [DOI: 10.1016/j.biomaterials.2022.121514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 04/01/2022] [Accepted: 04/04/2022] [Indexed: 11/02/2022]
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12
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Wollman AJ, Kioumourtzoglou D, Ward R, Gould GW, Bryant NJ. Large scale, single-cell FRET-based glucose uptake measurements within heterogeneous populations. iScience 2022; 25:104023. [PMID: 35313696 PMCID: PMC8933717 DOI: 10.1016/j.isci.2022.104023] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 12/01/2021] [Accepted: 03/01/2022] [Indexed: 11/30/2022] Open
Abstract
Fluorescent biosensors are powerful tools allowing the concentration of metabolites and small molecules, and other properties such as pH and molecular crowding to be measured inside live single cells. The technology has been hampered by lack of simple software to identify cells and quantify biosensor signals in single cells. We have developed a new software package, FRETzel, to address this gap and demonstrate its use by measuring insulin-stimulated glucose uptake in individual fat cells of varying sizes for the first time. Our results support the long-standing hypothesis that larger fat cells are less sensitive to insulin than smaller ones, a finding that has important implications for the battle against type 2 diabetes. FRETzel has been optimized using the messy and crowded environment of cultured adipocytes, demonstrating its utility for quantification of FRET biosensors in a wide range of other cell types, including fibroblasts and yeast via a simple user-friendly quantitative interface. FRETzel is a new software package for easy analysis of FRET signals in cells FRETzel is used to quantify glucose uptake in adipocytes of different sizes Reduced glucose uptake suggests that larger adipocytes have lower insulin sensitivity FRETzel is demonstrated on a range of cell types: yeast, fibroblasts, and adipocytes
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Affiliation(s)
- Adam J.M. Wollman
- Department of Biology and York Institute of Biomedical Research, University of York, York YO10 5DD, UK
- Biosciences Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
- Corresponding author
| | - Dimitrios Kioumourtzoglou
- Department of Biology and York Institute of Biomedical Research, University of York, York YO10 5DD, UK
| | - Rebecca Ward
- Department of Biology and York Institute of Biomedical Research, University of York, York YO10 5DD, UK
| | - Gwyn W. Gould
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, 161 Cathedral Street, Glasgow G4 0RE, UK
- Corresponding author
| | - Nia J. Bryant
- Department of Biology and York Institute of Biomedical Research, University of York, York YO10 5DD, UK
- Corresponding author
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13
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Gomariz A, Portenier T, Nombela-Arrieta C, Goksel O. Probabilistic spatial analysis in quantitative microscopy with uncertainty-aware cell detection using deep Bayesian regression. SCIENCE ADVANCES 2022; 8:eabi8295. [PMID: 35119934 PMCID: PMC8816343 DOI: 10.1126/sciadv.abi8295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 12/14/2021] [Indexed: 06/14/2023]
Abstract
The investigation of biological systems with three-dimensional microscopy demands automatic cell identification methods that not only are accurate but also can imply the uncertainty in their predictions. The use of deep learning to regress density maps is a popular successful approach for extracting cell coordinates from local peaks in a postprocessing step, which then, however, hinders any meaningful probabilistic output. We propose a framework that can operate on large microscopy images and output probabilistic predictions (i) by integrating deep Bayesian learning for the regression of uncertainty-aware density maps, where peak detection algorithms generate cell proposals, and (ii) by learning a mapping from prediction proposals to a probabilistic space that accurately represents the chances of a successful prediction. Using these calibrated predictions, we propose a probabilistic spatial analysis with Monte Carlo sampling. We demonstrate this in a bone marrow dataset, where our proposed methods reveal spatial patterns that are otherwise undetectable.
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Affiliation(s)
- Alvaro Gomariz
- Computer-assisted Applications in Medicine, ETH Zurich, Zurich, Switzerland
- Department of Medical Oncology and Hematology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Tiziano Portenier
- Computer-assisted Applications in Medicine, ETH Zurich, Zurich, Switzerland
| | - César Nombela-Arrieta
- Department of Medical Oncology and Hematology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Orcun Goksel
- Computer-assisted Applications in Medicine, ETH Zurich, Zurich, Switzerland
- Centre for Image Analysis, Department of Information Technology, Uppsala University, Uppsala, Sweden
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14
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Gai H, Wang Y, Chan LLH, Chiu B. Identification of Retinal Ganglion Cells from β-III Stained Fluorescent Microscopic Images. J Digit Imaging 2021; 33:1352-1363. [PMID: 32705432 DOI: 10.1007/s10278-020-00365-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
Abstract
Optic nerve crush in mouse model is widely used for investigating the course following retinal ganglion cell (RGCs) injury. Manual cell counting from β-III tubulin stained microscopic images has been routinely performed to monitor RGCs after an optic nerve crush injury, but is time-consuming and prone to observer variability. This paper describes an automatic technique for RGC identification. We developed and validated (i) a sensitive cell candidate segmentation scheme and (ii) a classifier that removed false positives while retaining true positives. Two major contributions were made in cell candidate segmentation. First, a homomorphic filter was designed to adjust for the inhomogeneous illumination caused by uneven penetration of β-III tubulin antibody. Second, the optimal segmentation parameters for cell detection are highly image-specific. To address this issue, we introduced an offline-online parameter tuning approach. Offline tuning optimized model parameters based on training images and online tuning further optimized the parameters at the testing stage without needing access to the ground truth. In the cell identification stage, 31 geometric, statistical and textural features were extracted from each segmented cell candidate, which was subsequently classified as true or false positives by support vector machine. The homomorphic filter and the online parameter tuning approach together increased cell recall by 28%. The entire pipeline attained a recall, precision and coefficient of determination (r2) of 85.3%, 97.1% and 0.994. The availability of the proposed pipeline will allow efficient, accurate and reproducible RGC quantification required for assessing the death/survival of RGCs in disease models.
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Affiliation(s)
- He Gai
- Department of Electrical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong
| | - Yi Wang
- Department of Electrical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong
| | - Leanne L H Chan
- Department of Electrical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong
| | - Bernard Chiu
- Department of Electrical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong.
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15
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Park J, Choi W, Tiesmeyer S, Long B, Borm LE, Garren E, Nguyen TN, Tasic B, Codeluppi S, Graf T, Schlesner M, Stegle O, Eils R, Ishaque N. Cell segmentation-free inference of cell types from in situ transcriptomics data. Nat Commun 2021; 12:3545. [PMID: 34112806 PMCID: PMC8192952 DOI: 10.1038/s41467-021-23807-4] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 05/14/2021] [Indexed: 12/24/2022] Open
Abstract
Multiplexed fluorescence in situ hybridization techniques have enabled cell-type identification, linking transcriptional heterogeneity with spatial heterogeneity of cells. However, inaccurate cell segmentation reduces the efficacy of cell-type identification and tissue characterization. Here, we present a method called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM), a robust cell segmentation-free computational framework for identifying cell-types and tissue domains in 2D and 3D. SSAM is applicable to a variety of in situ transcriptomics techniques and capable of integrating prior knowledge of cell types. We apply SSAM to three mouse brain tissue images: the somatosensory cortex imaged by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. Here, we show that SSAM detects regions occupied by known cell types that were previously missed and discovers new cell types.
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Affiliation(s)
- Jeongbin Park
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Digital Health Center, Kapelle-Ufer 2, 10117, Berlin, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
- Division of Computational Genomics and System Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wonyl Choi
- Department of Computer Science, Boston University, Boston, MA, USA
| | - Sebastian Tiesmeyer
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Digital Health Center, Kapelle-Ufer 2, 10117, Berlin, Germany
| | - Brian Long
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Lars E Borm
- Division of molecular neurobiology, Department of medical biochemistry and biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Emma Garren
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Simone Codeluppi
- Division of molecular neurobiology, Department of medical biochemistry and biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Tobias Graf
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Digital Health Center, Kapelle-Ufer 2, 10117, Berlin, Germany
| | - Matthias Schlesner
- Bioinformatics and Omics Data Analytics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Oliver Stegle
- Division of Computational Genomics and System Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Roland Eils
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Digital Health Center, Kapelle-Ufer 2, 10117, Berlin, Germany.
- Health Data Science Unit, Heidelberg University Hospital, Heidelberg, Germany.
| | - Naveed Ishaque
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Digital Health Center, Kapelle-Ufer 2, 10117, Berlin, Germany.
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16
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Wang Z, Yin L, Mao S, Wang Z. Segmentation of the Haematoxylin and Eosin Stained Muscle Cell Images—A Comparative Study. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2021. [DOI: 10.1166/jmihi.2021.3449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The effective detection of muscle cells, the accurate counting of their numbers and the analysis of their morphological features have great importance in biomedical research. At present, the quantification of muscle cell and the computation of their cross-sectional areas (CSA) are still
manual or semi-automated, and with the increase of the image number, the manual or semi-automated methods might become intractable. Hence, the automatic methods are very desirable, which motivated the developments of many muscle cell segmentation methods. In this paper, three methods, SDDM,
CELLSEGM and SMASH are compared and evaluated with 100 images with over 6000 cells. The Dices computed by SDDM, CELLSEGM and SMASH are 97.38%, 89.85% and 90.08% respectively. The average differences between the calculated cross-sectional areas and the ground truths by SDDM, CELLSEGM and SMASH
are 5.14%, 10.76% and 7.97% respectively.
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Affiliation(s)
- Zihao Wang
- Shandong University of Technology, 255000, Zibo City, Shandong Province, China
| | - Liju Yin
- Shandong University of Technology, 255000, Zibo City, Shandong Province, China
| | - Shuai Mao
- Shandong University of Technology, 255000, Zibo City, Shandong Province, China
| | - Zhenzhou Wang
- Shandong University of Technology, 255000, Zibo City, Shandong Province, China
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17
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Kim CK, Sanchez MI, Hoerbelt P, Fenno LE, Malenka RC, Deisseroth K, Ting AY. A Molecular Calcium Integrator Reveals a Striatal Cell Type Driving Aversion. Cell 2020; 183:2003-2019.e16. [PMID: 33308478 PMCID: PMC9839359 DOI: 10.1016/j.cell.2020.11.015] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 09/18/2020] [Accepted: 11/06/2020] [Indexed: 01/17/2023]
Abstract
The ability to record transient cellular events in the DNA or RNA of cells would enable precise, large-scale analysis, selection, and reprogramming of heterogeneous cell populations. Here, we report a molecular technology for stable genetic tagging of cells that exhibit activity-related increases in intracellular calcium concentration (FLiCRE). We used FLiCRE to transcriptionally label activated neural ensembles in the nucleus accumbens of the mouse brain during brief stimulation of aversive inputs. Using single-cell RNA sequencing, we detected FLiCRE transcripts among the endogenous transcriptome, providing simultaneous readout of both cell-type and calcium activation history. We identified a cell type in the nucleus accumbens activated downstream of long-range excitatory projections. Taking advantage of FLiCRE's modular design, we expressed an optogenetic channel selectively in this cell type and showed that direct recruitment of this otherwise genetically inaccessible population elicits behavioral aversion. The specificity and minute resolution of FLiCRE enables molecularly informed characterization, manipulation, and reprogramming of activated cellular ensembles.
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Affiliation(s)
- Christina K. Kim
- Department of Genetics, Stanford University, Stanford, CA 94305, USA.,These authors contributed equally to this work
| | - Mateo I. Sanchez
- Department of Genetics, Stanford University, Stanford, CA 94305, USA.,Chan Zuckerberg Biohub, San Francisco, CA 94158, USA.,These authors contributed equally to this work
| | - Paul Hoerbelt
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
| | - Lief E. Fenno
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA.,Department of Bioengineering, Stanford University, Stanford, CA 94035, USA.,Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Robert C. Malenka
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
| | - Karl Deisseroth
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA.,Department of Bioengineering, Stanford University, Stanford, CA 94035, USA.,Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.,Correspondence to: or
| | - Alice Y. Ting
- Department of Genetics, Stanford University, Stanford, CA 94305, USA.,Chan Zuckerberg Biohub, San Francisco, CA 94158, USA.,Department of Biology, Stanford University, Stanford, CA 94305, USA.,Lead contact:
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18
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Leonavicius K, Royer C, Miranda AMA, Tyser RCV, Kip A, Srinivas S. Spatial protein analysis in developing tissues: a sampling-based image processing approach. Philos Trans R Soc Lond B Biol Sci 2020; 375:20190560. [PMID: 32829691 PMCID: PMC7482225 DOI: 10.1098/rstb.2019.0560] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/22/2020] [Indexed: 11/19/2022] Open
Abstract
Advances in fluorescence microscopy approaches have made it relatively easy to generate multi-dimensional image volumes and have highlighted the need for flexible image analysis tools for the extraction of quantitative information from such data. Here we demonstrate that by focusing on simplified feature-based nuclear segmentation and probabilistic cytoplasmic detection we can create a tool that is able to extract geometry-based information from diverse mammalian tissue images. Our open-source image analysis platform, called 'SilentMark', can cope with three-dimensional noisy images and with crowded fields of cells to quantify signal intensity in different cellular compartments. Additionally, it provides tissue geometry related information, which allows one to quantify protein distribution with respect to marked regions of interest. The lightweight SilentMark algorithms have the advantage of not requiring multiple processors, graphics cards or training datasets and can be run even with just several hundred megabytes of memory. This makes it possible to use the method as a Web application, effectively eliminating setup hurdles and compatibility issues with operating systems. We test this platform on mouse pre-implantation embryos, embryonic stem cell-derived embryoid bodies and mouse embryonic heart, and relate protein localization to tissue geometry. This article is part of a discussion meeting issue 'Contemporary morphogenesis'.
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19
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Baybay EK, Esposito E, Hauf S. Pomegranate: 2D segmentation and 3D reconstruction for fission yeast and other radially symmetric cells. Sci Rep 2020; 10:16580. [PMID: 33024177 PMCID: PMC7538417 DOI: 10.1038/s41598-020-73597-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 09/14/2020] [Indexed: 11/09/2022] Open
Abstract
Three-dimensional (3D) segmentation of cells in microscopy images is crucial to accurately capture signals that extend across optical sections. Using brightfield images for segmentation has the advantage of being minimally phototoxic and leaving all other channels available for signals of interest. However, brightfield images only readily provide information for two-dimensional (2D) segmentation. In radially symmetric cells, such as fission yeast and many bacteria, this 2D segmentation can be computationally extruded into the third dimension. However, current methods typically make the simplifying assumption that cells are straight rods. Here, we report Pomegranate, a pipeline that performs the extrusion into 3D using spheres placed along the topological skeletons of the 2D-segmented regions. The diameter of these spheres adapts to the cell diameter at each position. Thus, Pomegranate accurately represents radially symmetric cells in 3D even if cell diameter varies and regardless of whether a cell is straight, bent or curved. We have tested Pomegranate on fission yeast and demonstrate its ability to 3D segment wild-type cells as well as classical size and shape mutants. The pipeline is available as a macro for the open-source image analysis software Fiji/ImageJ. 2D segmentations created within or outside Pomegranate can serve as input, thus making this a valuable extension to the image analysis portfolio already available for fission yeast and other radially symmetric cell types.
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Affiliation(s)
- Erod Keaton Baybay
- Department of Biological Sciences and Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA, USA.
| | - Eric Esposito
- Department of Biological Sciences and Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA, USA
| | - Silke Hauf
- Department of Biological Sciences and Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA, USA.
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20
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Ren H, Zhao M, Liu B, Yao R, Liu Q, Ren Z, Wu Z, Gao Z, Yang X, Tang C. Cellbow: a robust customizable cell segmentation program. QUANTITATIVE BIOLOGY 2020. [DOI: 10.1007/s40484-020-0213-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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21
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Maynard KR, Tippani M, Takahashi Y, Phan BN, Hyde TM, Jaffe AE, Martinowich K. dotdotdot: an automated approach to quantify multiplex single molecule fluorescent in situ hybridization (smFISH) images in complex tissues. Nucleic Acids Res 2020; 48:e66. [PMID: 32383753 PMCID: PMC7293004 DOI: 10.1093/nar/gkaa312] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 04/13/2020] [Accepted: 04/20/2020] [Indexed: 02/06/2023] Open
Abstract
Multiplex single-molecule fluorescent in situ hybridization (smFISH) is a powerful method for validating RNA sequencing and emerging spatial transcriptomic data, but quantification remains a computational challenge. We present a framework for generating and analyzing smFISH data in complex tissues while overcoming autofluorescence and increasing multiplexing capacity. We developed dotdotdot (https://github.com/LieberInstitute/dotdotdot) as a corresponding software package to quantify RNA transcripts in single nuclei and perform differential expression analysis. We first demonstrate robustness of our platform in single mouse neurons by quantifying differential expression of activity-regulated genes. We then quantify spatial gene expression in human dorsolateral prefrontal cortex (DLPFC) using spectral imaging and dotdotdot to mask lipofuscin autofluorescence. We lastly apply machine learning to predict cell types and perform downstream cell type-specific expression analysis. In summary, we provide experimental workflows, imaging acquisition and analytic strategies for quantification and biological interpretation of smFISH data in complex tissues.
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Affiliation(s)
- Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, USA
| | - Madhavi Tippani
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, USA
| | - Yoichiro Takahashi
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, USA
| | - BaDoi N Phan
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, USA.,Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.,Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Andrew E Jaffe
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, USA.,Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.,Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Department of Mental Health, Johns Hopkins University, Baltimore, MD, USA.,Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Keri Martinowich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, USA.,Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.,Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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22
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Ellis PW, Nambisan J, Fernandez-Nieves A. Coherence-enhanced diffusion filtering applied to partially-ordered fluids. Mol Phys 2020. [DOI: 10.1080/00268976.2020.1725167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Perry W. Ellis
- School of Physics, Georgia Institute of Technology, Atlanta, GA, USA
| | | | - Alberto Fernandez-Nieves
- School of Physics, Georgia Institute of Technology, Atlanta, GA, USA
- Department of Condensed Matter Physics, University of Barcelona, Barcelona, Spain
- ICREA-Institucio Catalana de Recerca i Estudis Avancats, Barcelona, Spain
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23
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Takko H, Pajanoja C, Kurtzeborn K, Hsin J, Kuure S, Kerosuo L. ShapeMetrics: A userfriendly pipeline for 3D cell segmentation and spatial tissue analysis. Dev Biol 2020; 462:7-19. [PMID: 32061886 DOI: 10.1016/j.ydbio.2020.02.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 01/29/2020] [Accepted: 02/01/2020] [Indexed: 12/20/2022]
Abstract
The demand for single-cell level data is constantly increasing within life sciences. In order to meet this demand, robust cell segmentation methods that can tackle challenging in vivo tissues with complex morphology are required. However, currently available cell segmentation and volumetric analysis methods perform poorly on 3D images. Here, we generated ShapeMetrics, a MATLAB-based script that segments cells in 3D and, by performing unbiased clustering using a heatmap, separates the cells into subgroups according to their volumetric and morphological differences. The cells can be accurately segregated according to different biologically meaningful features such as cell ellipticity, longest axis, cell elongation, or the ratio between cell volume and surface area. Our machine learning based script enables dissection of a large amount of novel data from microscope images in addition to the traditional information based on fluorescent biomarkers. Furthermore, the cells in different subgroups can be spatially mapped back to their original locations in the tissue image to help elucidate their roles in their respective morphological contexts. In order to facilitate the transition from bulk analysis to single-cell level accuracy, we emphasize the user-friendliness of our method by providing detailed step-by-step instructions through the pipeline hence aiming to reach users with less experience in computational biology.
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Affiliation(s)
- Heli Takko
- Department of Biochemistry and Developmental Biology, Biomedicum, University of Helsinki, Finland
| | - Ceren Pajanoja
- Department of Biochemistry and Developmental Biology, Biomedicum, University of Helsinki, Finland; National Institute of Dental and Craniofacial Research, National Institutes of Health, Neural Crest Development and Disease Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
| | - Kristen Kurtzeborn
- Department of Biochemistry and Developmental Biology, Biomedicum, University of Helsinki, Finland; Stem Cells and Metabolism Research Program, Faculty of Medicine, University of Helsinki, Finland
| | - Jenny Hsin
- National Institute of Dental and Craniofacial Research, National Institutes of Health, Neural Crest Development and Disease Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
| | - Satu Kuure
- Department of Biochemistry and Developmental Biology, Biomedicum, University of Helsinki, Finland; Stem Cells and Metabolism Research Program, Faculty of Medicine, University of Helsinki, Finland; GM-unit, Laboratory Animal Centre, Helsinki Institute of Life Science, University of Helsinki, Finland
| | - Laura Kerosuo
- Department of Biochemistry and Developmental Biology, Biomedicum, University of Helsinki, Finland; National Institute of Dental and Craniofacial Research, National Institutes of Health, Neural Crest Development and Disease Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA.
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24
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TrkB Signaling Influences Gene Expression in Cortistatin-Expressing Interneurons. eNeuro 2020; 7:ENEURO.0310-19.2019. [PMID: 31941661 PMCID: PMC7031852 DOI: 10.1523/eneuro.0310-19.2019] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 11/14/2019] [Accepted: 12/04/2019] [Indexed: 01/02/2023] Open
Abstract
Brain-derived neurotrophic factor (BDNF) signals through its cognate receptor tropomyosin receptor kinase B (TrkB) to promote the function of several classes of inhibitory interneurons. We previously reported that loss of BDNF-TrkB signaling in cortistatin (Cort)-expressing interneurons leads to behavioral hyperactivity and spontaneous seizures in mice. We performed bulk RNA sequencing (RNA-seq) from the cortex of mice with disruption of BDNF-TrkB signaling in cortistatin interneurons, and identified differential expression of genes important for excitatory neuron function. Using translating ribosome affinity purification and RNA-seq, we define a molecular profile for Cort-expressing inhibitory neurons and subsequently compare the translatome of normal and TrkB-depleted Cort neurons, revealing alterations in calcium signaling and axon development. Several of the genes enriched in Cort neurons and differentially expressed in TrkB-depleted neurons are also implicated in autism and epilepsy. Our findings highlight TrkB-dependent molecular pathways as critical for the maturation of inhibitory interneurons and support the hypothesis that loss of BDNF signaling in Cort interneurons leads to altered excitatory/inhibitory balance.
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25
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Hallock HL, Quillian HM, Mai Y, Maynard KR, Hill JL, Martinowich K. Manipulation of a genetically and spatially defined sub-population of BDNF-expressing neurons potentiates learned fear and decreases hippocampal-prefrontal synchrony in mice. Neuropsychopharmacology 2019; 44:2239-2246. [PMID: 31170726 PMCID: PMC6898598 DOI: 10.1038/s41386-019-0429-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 05/10/2019] [Accepted: 05/29/2019] [Indexed: 12/30/2022]
Abstract
Brain-derived neurotrophic factor (BDNF) signaling regulates synaptic plasticity in the hippocampus (HC) and prefrontal cortex (PFC), and has been extensively linked with fear memory expression in rodents. Notably, disrupting BDNF production from promoter IV-derived transcripts enhances fear expression in mice, and decreases fear-associated HC-PFC synchrony, suggesting that Bdnf transcription from promoter IV plays a key role in HC-PFC function during fear memory retrieval. To better understand how promoter IV-derived BDNF controls HC-PFC connectivity and fear expression, we generated a viral construct that selectively targets cells expressing promoter IV-derived Bdnf transcripts ("p4-cells") for tamoxifen-inducible Cre-mediated recombination (AAV8-p4Bdnf-ERT2CreERT2-PEST). Using this construct, we found that ventral hippocampal (vHC) p4-cells are recruited during fear expression, and that activation of these cells causes exaggerated fear expression that co-occurs with disrupted vHC-PFC synchrony in mice. Our data highlight how this novel construct can be used to interrogate genetically defined cell types that selectively contribute to BDNF-dependent behaviors.
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Affiliation(s)
- Henry L. Hallock
- grid.429552.dThe Lieber Institute for Brain Development, 855N. Wolfe St., Suite 300, Baltimore, MD USA
| | - Henry M. Quillian
- grid.429552.dThe Lieber Institute for Brain Development, 855N. Wolfe St., Suite 300, Baltimore, MD USA
| | - Yishan Mai
- grid.429552.dThe Lieber Institute for Brain Development, 855N. Wolfe St., Suite 300, Baltimore, MD USA
| | - Kristen R. Maynard
- grid.429552.dThe Lieber Institute for Brain Development, 855N. Wolfe St., Suite 300, Baltimore, MD USA
| | - Julia L. Hill
- grid.429552.dThe Lieber Institute for Brain Development, 855N. Wolfe St., Suite 300, Baltimore, MD USA
| | - Keri Martinowich
- The Lieber Institute for Brain Development, 855N. Wolfe St., Suite 300, Baltimore, MD, USA. .,Department of Psychiatry, The Johns Hopkins University School of Medicine, Baltimore, MD, USA. .,Department of Neuroscience, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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26
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Gómez-de-Mariscal E, Maška M, Kotrbová A, Pospíchalová V, Matula P, Muñoz-Barrutia A. Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images. Sci Rep 2019; 9:13211. [PMID: 31519998 PMCID: PMC6744556 DOI: 10.1038/s41598-019-49431-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 08/05/2019] [Indexed: 02/07/2023] Open
Abstract
Small extracellular vesicles (sEVs) are cell-derived vesicles of nanoscale size (~30-200 nm) that function as conveyors of information between cells, reflecting the cell of their origin and its physiological condition in their content. Valuable information on the shape and even on the composition of individual sEVs can be recorded using transmission electron microscopy (TEM). Unfortunately, sample preparation for TEM image acquisition is a complex procedure, which often leads to noisy images and renders automatic quantification of sEVs an extremely difficult task. We present a completely deep-learning-based pipeline for the segmentation of sEVs in TEM images. Our method applies a residual convolutional neural network to obtain fine masks and use the Radon transform for splitting clustered sEVs. Using three manually annotated datasets that cover a natural variability typical for sEV studies, we show that the proposed method outperforms two different state-of-the-art approaches in terms of detection and segmentation performance. Furthermore, the diameter and roundness of the segmented vesicles are estimated with an error of less than 10%, which supports the high potential of our method in biological applications.
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Affiliation(s)
- Estibaliz Gómez-de-Mariscal
- Bioengineering and Aerospace Engineering Department, Universidad Carlos III de Madrid, Leganés, 28911, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, 28007, Spain
| | - Martin Maška
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, 602 00, Czech Republic
| | - Anna Kotrbová
- Department of Experimental Biology, Faculty of Science, Masaryk University, Brno, 611 37, Czech Republic
| | - Vendula Pospíchalová
- Department of Experimental Biology, Faculty of Science, Masaryk University, Brno, 611 37, Czech Republic
| | - Pavel Matula
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, 602 00, Czech Republic
| | - Arrate Muñoz-Barrutia
- Bioengineering and Aerospace Engineering Department, Universidad Carlos III de Madrid, Leganés, 28911, Spain.
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, 28007, Spain.
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27
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Corrigan AM, Karlsson J, Wildenhain J, Knerr L, Ölwegård-Halvarsson M, Karlsson M, Lünse S, Wang Y. IA-Lab: A MATLAB framework for efficient microscopy image analysis development, applied to quantifying intracellular transport of internalized peptide-drug conjugate. PLoS One 2019; 14:e0220627. [PMID: 31369634 PMCID: PMC6675096 DOI: 10.1371/journal.pone.0220627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 07/19/2019] [Indexed: 11/30/2022] Open
Abstract
This work presents a MATLAB-based software package for high-throughput microscopy image analysis development, making such development more accessible for a large user community. The toolbox provides a GUI and a number of analysis workflows, and can serve as a general framework designed to allow for easy extension. For a new application, only a minor part of the object-oriented code needs to be replaced by new components, making development efficient. This makes it possible to quickly develop solutions for analysis not available in existing tools. We show its use in making a tool for quantifying intracellular transport of internalized peptide-drug conjugates. The code is freely available as open source on GitHub (https://github.com/amcorrigan/ia-lab)
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Affiliation(s)
- Adam M. Corrigan
- Discovery Sciences, R&D, AstraZeneca, Cambridge, United Kingdom
- * E-mail:
| | - Johan Karlsson
- Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Jan Wildenhain
- Discovery Sciences, R&D, AstraZeneca, Cambridge, United Kingdom
| | - Laurent Knerr
- Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Maria Ölwegård-Halvarsson
- Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Maria Karlsson
- Research and Early Development, Respiratory, Inflammation and Autoimmune, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Svenja Lünse
- Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Yinhai Wang
- Discovery Sciences, R&D, AstraZeneca, Cambridge, United Kingdom
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28
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Pamula MC, Carlini L, Forth S, Verma P, Suresh S, Legant WR, Khodjakov A, Betzig E, Kapoor TM. High-resolution imaging reveals how the spindle midzone impacts chromosome movement. J Cell Biol 2019; 218:2529-2544. [PMID: 31248912 PMCID: PMC6683753 DOI: 10.1083/jcb.201904169] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 05/21/2019] [Accepted: 05/30/2019] [Indexed: 12/11/2022] Open
Abstract
Microtubule bundles in the spindle midzone have been reported to either promote or hinder chromosome movement. Pamula et al. examine the assembly dynamics of midzone microtubule bundles during anaphase and how chromosome segregation is impacted by aberrant bundle assembly. In the spindle midzone, microtubules from opposite half-spindles form bundles between segregating chromosomes. Microtubule bundles can either push or restrict chromosome movement during anaphase in different cellular contexts, but how these activities are achieved remains poorly understood. Here, we use high-resolution live-cell imaging to analyze individual microtubule bundles, growing filaments, and chromosome movement in dividing human cells. Within bundles, filament overlap length marked by the cross-linking protein PRC1 decreases during anaphase as chromosome segregation slows. Filament ends within microtubule bundles appear capped despite dynamic PRC1 turnover and submicrometer proximity to growing microtubules. Chromosome segregation distance and rate are increased in two human cell lines when microtubule bundle assembly is prevented via PRC1 knockdown. Upon expressing a mutant PRC1 with reduced microtubule affinity, bundles assemble but chromosome hypersegregation is still observed. We propose that microtubule overlap length reduction, typically linked to pushing forces generated within filament bundles, is needed to properly restrict spindle elongation and position chromosomes within daughter cells.
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Affiliation(s)
- Melissa C Pamula
- Laboratory of Chemistry and Cell Biology, The Rockefeller University, New York, NY
| | - Lina Carlini
- Laboratory of Chemistry and Cell Biology, The Rockefeller University, New York, NY
| | - Scott Forth
- Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY
| | - Priyanka Verma
- Department of Cancer Biology, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Subbulakshmi Suresh
- Laboratory of Chemistry and Cell Biology, The Rockefeller University, New York, NY
| | - Wesley R Legant
- Department of Pharmacology, University of North Carolina, Chapel Hill, NC.,Joint Department of Biomedical Engineering, University of North Carolina, Chapel Hill, and North Carolina State University, Raleigh, NC
| | - Alexey Khodjakov
- Wadsworth Center, New York State Department of Health, Albany, NY
| | - Eric Betzig
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA.,Department of Physics and Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA
| | - Tarun M Kapoor
- Laboratory of Chemistry and Cell Biology, The Rockefeller University, New York, NY
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29
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Lotfollahi M, Berisha S, Saadatifard L, Montier L, Žiburkus J, Mayerich D. Three-dimensional GPU-accelerated active contours for automated localization of cells in large images. PLoS One 2019; 14:e0215843. [PMID: 31173591 PMCID: PMC6555506 DOI: 10.1371/journal.pone.0215843] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 04/09/2019] [Indexed: 01/17/2023] Open
Abstract
Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. Successful cell segmentation algorithms rely identifying seed points, and are highly sensitive to variablility in cell size. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional contour evolution that extends previous work on fast two-dimensional snakes. We provide a formulation for optimization on 3D images, as well as a strategy for accelerating computation on consumer graphics hardware. The proposed software takes advantage of Monte-Carlo sampling schemes in order to speed up convergence and reduce thread divergence. Experimental results show that this method provides superior performance for large 2D and 3D cell localization tasks when compared to existing methods on large 3D brain images.
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Affiliation(s)
- Mahsa Lotfollahi
- Department of Electrical and Computer engineering, University of Houston, Houston, TX, United States of America
| | - Sebastian Berisha
- Department of Electrical and Computer engineering, University of Houston, Houston, TX, United States of America
| | - Leila Saadatifard
- Department of Electrical and Computer engineering, University of Houston, Houston, TX, United States of America
| | - Laura Montier
- Department of Biology and Biochemistry, University of Houston, TX, United States of America
| | - Jokūbas Žiburkus
- Department of Biology and Biochemistry, University of Houston, TX, United States of America
| | - David Mayerich
- Department of Electrical and Computer engineering, University of Houston, Houston, TX, United States of America
- * E-mail:
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30
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Zhang H, Söderholm N, Sandblad L, Wiklund K, Andersson M. DSeg: A Dynamic Image Segmentation Program to Extract Backbone Patterns for Filamentous Bacteria and Hyphae Structures. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2019; 25:711-719. [PMID: 30894244 DOI: 10.1017/s1431927619000308] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Analysis of numerous filamentous structures in an image is often limited by the ability of algorithms to accurately segment complex structures or structures within a dense population. It is even more problematic if these structures continuously grow when recording a time-series of images. To overcome these issues we present DSeg; an image analysis program designed to process time-series image data, as well as single images, to segment filamentous structures. The program includes a robust binary level-set algorithm modified to use size constraints, edge intensity, and past information. We verify our algorithms using synthetic data, differential interference contrast images of filamentous prokaryotes, and transmission electron microscopy images of bacterial adhesion fimbriae. DSeg includes automatic segmentation, tools for analysis, and drift correction, and outputs statistical data such as persistence length, growth rate, and growth direction. The program is available at Sourceforge.
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Affiliation(s)
- Hanqing Zhang
- Department of Physics,Umeå University,901 87 Umeå,Sweden
| | - Niklas Söderholm
- Department of Molecular Biology,Umeå University,901 87 Umeå,Sweden
| | - Linda Sandblad
- Department of Molecular Biology,Umeå University,901 87 Umeå,Sweden
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31
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Goktas P, Sukharevsky IO, Larkin S, Kuypers FA, Yalcin O, Altintas A. Image‐Based Flow Cytometry and Angle‐Resolved Light Scattering to Define the Sickling Process. Cytometry A 2019; 95:488-498. [DOI: 10.1002/cyto.a.23756] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 03/15/2019] [Accepted: 03/21/2019] [Indexed: 12/16/2022]
Affiliation(s)
- Polat Goktas
- Department of Electrical and Electronics EngineeringBilkent University Ankara 06800 Turkey
- Department of Physiology, School of MedicineKoc University Istanbul 34450 Turkey
| | - Ilya O. Sukharevsky
- Department of Electrical and Computer Engineering, Chair of High‐Frequency EngineeringTechnical University of Munich Munich 80333 Germany
| | - Sandra Larkin
- Children's Hospital Oakland Research Institute Oakland California, 94609
| | - Frans A. Kuypers
- Children's Hospital Oakland Research Institute Oakland California, 94609
| | - Ozlem Yalcin
- Department of Physiology, School of MedicineKoc University Istanbul 34450 Turkey
| | - Ayhan Altintas
- Department of Electrical and Electronics EngineeringBilkent University Ankara 06800 Turkey
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32
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Kim CK, Cho KF, Kim MW, Ting AY. Luciferase-LOV BRET enables versatile and specific transcriptional readout of cellular protein-protein interactions. eLife 2019; 8:43826. [PMID: 30942168 PMCID: PMC6447360 DOI: 10.7554/elife.43826] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Accepted: 03/16/2019] [Indexed: 12/21/2022] Open
Abstract
Technologies that convert transient protein-protein interactions (PPIs) into stable expression of a reporter gene are useful for genetic selections, high-throughput screening, and multiplexing with omics technologies. We previously reported SPARK (Kim et al., 2017), a transcription factor that is activated by the coincidence of blue light and a PPI. Here, we report an improved, second-generation SPARK2 that incorporates a luciferase moiety to control the light-sensitive LOV domain. SPARK2 can be temporally gated by either external light or addition of a small-molecule luciferin, which causes luciferase to open LOV via proximity-dependent BRET. Furthermore, the nested 'AND' gate design of SPARK2-in which both protease recruitment to the membrane-anchored transcription factor and LOV domain opening are regulated by the PPI of interest-yields a lower-background system and improved PPI specificity. We apply SPARK2 to high-throughput screening for GPCR agonists and for the detection of trans-cellular contacts, all with versatile transcriptional readout.
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Affiliation(s)
- Christina K Kim
- Department of Genetics, Stanford University, Stanford, United States
| | - Kelvin F Cho
- Cancer Biology Program, Stanford University, Stanford, United States
| | - Min Woo Kim
- Department of Genetics, Stanford University, Stanford, United States
| | - Alice Y Ting
- Department of Genetics, Stanford University, Stanford, United States.,Department of Biology, Stanford University, Stanford, United States.,Chan Zuckerberg Biohub, San Francisco, United States
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33
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Holenstein CN, Horvath A, Schär B, Schoenenberger AD, Bollhalder M, Goedecke N, Bartalena G, Otto O, Herbig M, Guck J, Müller DA, Snedeker JG, Silvan U. The relationship between metastatic potential and in vitro mechanical properties of osteosarcoma cells. Mol Biol Cell 2019; 30:887-898. [PMID: 30785850 PMCID: PMC6589788 DOI: 10.1091/mbc.e18-08-0545] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Osteosarcoma is the most frequent primary tumor of bone and is characterized by its high tendency to metastasize in lungs. Although treatment in cases of early diagnosis results in a 5-yr survival rate of nearly 60%, the prognosis for patients with secondary lesions at diagnosis is poor, and their 5-yr survival rate remains below 30%. In the present work, we have used a number of analytical methods to investigate the impact of increased metastatic potential on the biophysical properties and force generation of osteosarcoma cells. With that aim, we used two paired osteosarcoma cell lines, with each one comprising a parental line with low metastatic potential and its experimentally selected, highly metastatic form. Mechanical characterization was performed by means of atomic force microscopy, tensile biaxial deformation, and real-time deformability, and cell traction was measured using two-dimensional and micropost-based traction force microscopy. Our results reveal that the low metastatic osteosarcoma cells display larger spreading sizes and generate higher forces than the experimentally selected, highly malignant variants. In turn, the outcome of cell stiffness measurements strongly depends on the method used and the state of the probed cell, indicating that only a set of phenotyping methods provides the full picture of cell mechanics.
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Affiliation(s)
- Claude N Holenstein
- Biomechanics Laboratory, University Hospital Balgrist, University of Zürich, 8008 Zürich, Switzerland.,Institute for Biomechanics, ETH Zurich, 8008 Zürich, Switzerland
| | - Aron Horvath
- Biomechanics Laboratory, University Hospital Balgrist, University of Zürich, 8008 Zürich, Switzerland.,Institute for Biomechanics, ETH Zurich, 8008 Zürich, Switzerland
| | - Barbara Schär
- Biomechanics Laboratory, University Hospital Balgrist, University of Zürich, 8008 Zürich, Switzerland.,Institute for Biomechanics, ETH Zurich, 8008 Zürich, Switzerland
| | - Angelina D Schoenenberger
- Biomechanics Laboratory, University Hospital Balgrist, University of Zürich, 8008 Zürich, Switzerland.,Institute for Biomechanics, ETH Zurich, 8008 Zürich, Switzerland
| | - Maja Bollhalder
- Biomechanics Laboratory, University Hospital Balgrist, University of Zürich, 8008 Zürich, Switzerland.,Institute for Biomechanics, ETH Zurich, 8008 Zürich, Switzerland
| | - Nils Goedecke
- Institute for Biomechanics, ETH Zurich, 8008 Zürich, Switzerland
| | - Guido Bartalena
- Institute for Biomechanics, ETH Zurich, 8008 Zürich, Switzerland
| | - Oliver Otto
- Biotechnology Center, Technische Universität Dresden, 01307 Dresden, Germany.,Zentrum für Innovationskompetenz, Universität Greifswald, 17489 Greifswald, Germany
| | - Maik Herbig
- Biotechnology Center, Technische Universität Dresden, 01307 Dresden, Germany
| | - Jochen Guck
- Biotechnology Center, Technische Universität Dresden, 01307 Dresden, Germany
| | - Daniel A Müller
- Biomechanics Laboratory, University Hospital Balgrist, University of Zürich, 8008 Zürich, Switzerland
| | - Jess G Snedeker
- Biomechanics Laboratory, University Hospital Balgrist, University of Zürich, 8008 Zürich, Switzerland.,Institute for Biomechanics, ETH Zurich, 8008 Zürich, Switzerland
| | - Unai Silvan
- Biomechanics Laboratory, University Hospital Balgrist, University of Zürich, 8008 Zürich, Switzerland.,Institute for Biomechanics, ETH Zurich, 8008 Zürich, Switzerland
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34
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Wang Z. Cell Segmentation for Image Cytometry: Advances, Insufficiencies, and Challenges. Cytometry A 2018; 95:708-711. [DOI: 10.1002/cyto.a.23686] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 10/27/2018] [Accepted: 11/02/2018] [Indexed: 11/10/2022]
Affiliation(s)
- Zhenzhou Wang
- College of electrical and electronic engineeringShandong University of Technology 255000 Zibo City Shandong Province China
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35
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Microscopic malaria parasitemia diagnosis and grading on benchmark datasets. Microsc Res Tech 2018; 81:1042-1058. [DOI: 10.1002/jemt.23071] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Revised: 04/23/2018] [Accepted: 05/10/2018] [Indexed: 12/16/2022]
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36
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Maynard KR, Hobbs JW, Phan BN, Gupta A, Rajpurohit S, Williams C, Rajpurohit A, Shin JH, Jaffe AE, Martinowich K. BDNF-TrkB signaling in oxytocin neurons contributes to maternal behavior. eLife 2018; 7:33676. [PMID: 30192229 PMCID: PMC6135608 DOI: 10.7554/elife.33676] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2017] [Accepted: 09/02/2018] [Indexed: 12/18/2022] Open
Abstract
Brain-derived neurotrophic factor (Bdnf) transcription is controlled by several promoters, which drive expression of multiple transcripts encoding an identical protein. We previously reported that BDNF derived from promoters I and II is highly expressed in hypothalamus and is critical for regulating aggression in male mice. Here we report that BDNF loss from these promoters causes reduced sexual receptivity and impaired maternal care in female mice, which is concomitant with decreased oxytocin (Oxt) expression during development. We identify a novel link between BDNF signaling, oxytocin, and maternal behavior by demonstrating that ablation of TrkB selectively in OXT neurons partially recapitulates maternal care impairments observed in BDNF-deficient females. Using translating ribosome affinity purification and RNA-sequencing we define a molecular profile for OXT neurons and delineate how BDNF signaling impacts gene pathways critical for structural and functional plasticity. Our findings highlight BDNF as a modulator of sexually-dimorphic hypothalamic circuits that govern female-typical behaviors.
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Affiliation(s)
- Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, United States
| | - John W Hobbs
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, United States
| | - BaDoi N Phan
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, United States
| | - Amolika Gupta
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, United States
| | - Sumita Rajpurohit
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, United States
| | - Courtney Williams
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, United States
| | - Anandita Rajpurohit
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, United States
| | - Joo Heon Shin
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, United States
| | - Andrew E Jaffe
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, United States.,Department of Mental Health, Johns Hopkins University, Baltimore, United States.,Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States.,Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, United States
| | - Keri Martinowich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, United States.,Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, United States.,Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, United States
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37
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Rehman A, Abbas N, Saba T, Mahmood T, Kolivand H. Rouleaux red blood cells splitting in microscopic thin blood smear images via local maxima, circles drawing, and mapping with original RBCs. Microsc Res Tech 2018; 81:737-744. [DOI: 10.1002/jemt.23030] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Revised: 01/27/2018] [Accepted: 03/24/2018] [Indexed: 12/26/2022]
Affiliation(s)
- Amjad Rehman
- College of Computer and Information SystemsAl Yamamah UniversityRiyadh, 11512 Saudi Arabia
| | - Naveed Abbas
- Computer Science Department Islamia CollegeUniversity Peshawar Pakistan
| | - Tanzila Saba
- College of Computer and Information SciencesPrince Sultan UniversityRiyadh, 11586 Saudi Arabia
| | - Toqeer Mahmood
- Department of Computer ScienceUniversity of Engineering and TechnologyTaxila, 47050 Pakistan
| | - Hoshang Kolivand
- Department of Computer ScienceLiverpool John Moores UniversityLiverpool, L3 3AF United Kingdom
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38
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Georg M, Fernández-Cabada T, Bourguignon N, Karp P, Peñaherrera AB, Helguera G, Lerner B, Pérez MS, Mertelsmann R. Development of image analysis software for quantification of viable cells in microchips. PLoS One 2018; 13:e0193605. [PMID: 29494694 PMCID: PMC5832319 DOI: 10.1371/journal.pone.0193605] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 02/14/2018] [Indexed: 12/04/2022] Open
Abstract
Over the past few years, image analysis has emerged as a powerful tool for analyzing various cell biology parameters in an unprecedented and highly specific manner. The amount of data that is generated requires automated methods for the processing and analysis of all the resulting information. The software available so far are suitable for the processing of fluorescence and phase contrast images, but often do not provide good results from transmission light microscopy images, due to the intrinsic variation of the acquisition of images technique itself (adjustment of brightness / contrast, for instance) and the variability between image acquisition introduced by operators / equipment. In this contribution, it has been presented an image processing software, Python based image analysis for cell growth (PIACG), that is able to calculate the total area of the well occupied by cells with fusiform and rounded morphology in response to different concentrations of fetal bovine serum in microfluidic chips, from microscopy images in transmission light, in a highly efficient way.
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Affiliation(s)
- Maximilian Georg
- Department of Hematology and Oncology, University of Freiburg Medical Center, Freiburg, Germany
| | - Tamara Fernández-Cabada
- National Technological University (UTN), Regional Faculty from Haedo, Paris, Buenos Aires, Argentina
- Faculty of Engineering - Institute of Biomedical Engineering - University of Buenos Aires (UBA), Buenos Aires C1063ACV, Argentina
| | - Natalia Bourguignon
- National Technological University (UTN), Regional Faculty from Haedo, Paris, Buenos Aires, Argentina
- Faculty of Engineering - Institute of Biomedical Engineering - University of Buenos Aires (UBA), Buenos Aires C1063ACV, Argentina
| | - Paola Karp
- Biology and Experimental Medicine Institute (IBYME CONICET), Buenos Aires C1428ADN, Argentina
| | - Ana B. Peñaherrera
- National Technological University (UTN), Regional Faculty from Haedo, Paris, Buenos Aires, Argentina
- Faculty of Engineering - Institute of Biomedical Engineering - University of Buenos Aires (UBA), Buenos Aires C1063ACV, Argentina
| | - Gustavo Helguera
- Biology and Experimental Medicine Institute (IBYME CONICET), Buenos Aires C1428ADN, Argentina
| | - Betiana Lerner
- National Technological University (UTN), Regional Faculty from Haedo, Paris, Buenos Aires, Argentina
- Faculty of Engineering - Institute of Biomedical Engineering - University of Buenos Aires (UBA), Buenos Aires C1063ACV, Argentina
| | - Maximiliano S. Pérez
- National Technological University (UTN), Regional Faculty from Haedo, Paris, Buenos Aires, Argentina
- Faculty of Engineering - Institute of Biomedical Engineering - University of Buenos Aires (UBA), Buenos Aires C1063ACV, Argentina
- * E-mail: (RM); (MSP)
| | - Roland Mertelsmann
- Department of Hematology and Oncology, University of Freiburg Medical Center, Freiburg, Germany
- * E-mail: (RM); (MSP)
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39
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Desgrange A, Heliot C, Skovorodkin I, Akram SU, Heikkilä J, Ronkainen VP, Miinalainen I, Vainio SJ, Cereghini S. HNF1B controls epithelial organization and cell polarity during ureteric bud branching and collecting duct morphogenesis. Development 2017; 144:4704-4719. [PMID: 29158444 DOI: 10.1242/dev.154336] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Accepted: 11/03/2017] [Indexed: 12/16/2022]
Abstract
Kidney development depends crucially on proper ureteric bud branching giving rise to the entire collecting duct system. The transcription factor HNF1B is required for the early steps of ureteric bud branching, yet the molecular and cellular events regulated by HNF1B are poorly understood. We report that specific removal of Hnf1b from the ureteric bud leads to defective cell-cell contacts and apicobasal polarity during the early branching events. High-resolution ex vivo imaging combined with a membranous fluorescent reporter strategy show decreased mutant cell rearrangements during mitosis-associated cell dispersal and severe epithelial disorganization. Molecular analysis reveals downregulation of Gdnf-Ret pathway components and suggests that HNF1B acts both upstream and downstream of Ret signaling by directly regulating Gfra1 and Etv5 Subsequently, Hnf1b deletion leads to massively mispatterned ureteric tree network, defective collecting duct differentiation and disrupted tissue architecture, which leads to cystogenesis. Consistently, mRNA-seq analysis shows that the most impacted genes encode intrinsic cell-membrane components with transporter activity. Our study uncovers a fundamental and recurring role of HNF1B in epithelial organization during early ureteric bud branching and in further patterning and differentiation of the collecting duct system in mouse.
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Affiliation(s)
- Audrey Desgrange
- Sorbonne Universités, UPMC Université Paris 06, IBPS - UMR7622, F-75005 Paris, France .,CNRS, UMR7622, Institut de Biologie Paris-Seine (IBPS) - Developmental Biology Laboratory, F-75005 Paris, France
| | - Claire Heliot
- Sorbonne Universités, UPMC Université Paris 06, IBPS - UMR7622, F-75005 Paris, France.,CNRS, UMR7622, Institut de Biologie Paris-Seine (IBPS) - Developmental Biology Laboratory, F-75005 Paris, France
| | - Ilya Skovorodkin
- Faculty of Biochemistry and Molecular Medicine, Biocenter, University of Oulu; Laboratory of Developmental Biology, Biocenter Oulu and InfoTech, Department of Medical Biochemistry and Molecular Medicine, Oulu Center for Cell Matrix Research, 90220 Oulu, Finland
| | - Saad U Akram
- Center for Machine Vision Research and Signal Analysis (CMVS), University of Oulu, FIN-90014, Oulu, Finland
| | - Janne Heikkilä
- Center for Machine Vision Research and Signal Analysis (CMVS), University of Oulu, FIN-90014, Oulu, Finland
| | | | | | - Seppo J Vainio
- Faculty of Biochemistry and Molecular Medicine, Biocenter, University of Oulu; Laboratory of Developmental Biology, Biocenter Oulu and InfoTech, Department of Medical Biochemistry and Molecular Medicine, Oulu Center for Cell Matrix Research, 90220 Oulu, Finland
| | - Silvia Cereghini
- Sorbonne Universités, UPMC Université Paris 06, IBPS - UMR7622, F-75005 Paris, France .,CNRS, UMR7622, Institut de Biologie Paris-Seine (IBPS) - Developmental Biology Laboratory, F-75005 Paris, France
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40
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Xu M, Papageorgiou DP, Abidi SZ, Dao M, Zhao H, Karniadakis GE. A deep convolutional neural network for classification of red blood cells in sickle cell anemia. PLoS Comput Biol 2017; 13:e1005746. [PMID: 29049291 PMCID: PMC5654260 DOI: 10.1371/journal.pcbi.1005746] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Accepted: 08/29/2017] [Indexed: 11/18/2022] Open
Abstract
Sickle cell disease (SCD) is a hematological disorder leading to blood vessel occlusion accompanied by painful episodes and even death. Red blood cells (RBCs) of SCD patients have diverse shapes that reveal important biomechanical and bio-rheological characteristics, e.g. their density, fragility, adhesive properties, etc. Hence, having an objective and effective way of RBC shape quantification and classification will lead to better insights and eventual better prognosis of the disease. To this end, we have developed an automated, high-throughput, ex-vivo RBC shape classification framework that consists of three stages. First, we present an automatic hierarchical RBC extraction method to detect the RBC region (ROI) from the background, and then separate touching RBCs in the ROI images by applying an improved random walk method based on automatic seed generation. Second, we apply a mask-based RBC patch-size normalization method to normalize the variant size of segmented single RBC patches into uniform size. Third, we employ deep convolutional neural networks (CNNs) to realize RBC classification; the alternating convolution and pooling operations can deal with non-linear and complex patterns. Furthermore, we investigate the specific shape factor quantification for the classified RBC image data in order to develop a general multiscale shape analysis. We perform several experiments on raw microscopy image datasets from 8 SCD patients (over 7,000 single RBC images) through a 5-fold cross validation method both for oxygenated and deoxygenated RBCs. We demonstrate that the proposed framework can successfully classify sickle shape RBCs in an automated manner with high accuracy, and we also provide the corresponding shape factor analysis, which can be used synergistically with the CNN analysis for more robust predictions. Moreover, the trained deep CNN exhibits good performance even for a deoxygenated dataset and distinguishes the subtle differences in texture alteration inside the oxygenated and deoxygenated RBCs.
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Affiliation(s)
- Mengjia Xu
- Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China
- Division of Applied Mathematics, Brown University, Providence, Rhode Island, United States of America
| | - Dimitrios P. Papageorgiou
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Sabia Z. Abidi
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Ming Dao
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Hong Zhao
- Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China
| | - George Em Karniadakis
- Division of Applied Mathematics, Brown University, Providence, Rhode Island, United States of America
- * E-mail:
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41
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Magliaro C, Callara AL, Vanello N, Ahluwalia A. A Manual Segmentation Tool for Three-Dimensional Neuron Datasets. Front Neuroinform 2017; 11:36. [PMID: 28620293 PMCID: PMC5450622 DOI: 10.3389/fninf.2017.00036] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Accepted: 05/16/2017] [Indexed: 01/19/2023] Open
Abstract
To date, automated or semi-automated software and algorithms for segmentation of neurons from three-dimensional imaging datasets have had limited success. The gold standard for neural segmentation is considered to be the manual isolation performed by an expert. To facilitate the manual isolation of complex objects from image stacks, such as neurons in their native arrangement within the brain, a new Manual Segmentation Tool (ManSegTool) has been developed. ManSegTool allows user to load an image stack, scroll down the images and to manually draw the structures of interest stack-by-stack. Users can eliminate unwanted regions or split structures (i.e., branches from different neurons that are too close each other, but, to the experienced eye, clearly belong to a unique cell), to view the object in 3D and save the results obtained. The tool can be used for testing the performance of a single-neuron segmentation algorithm or to extract complex objects, where the available automated methods still fail. Here we describe the software's main features and then show an example of how ManSegTool can be used to segment neuron images acquired using a confocal microscope. In particular, expert neuroscientists were asked to segment different neurons from which morphometric variables were subsequently extracted as a benchmark for precision. In addition, a literature-defined index for evaluating the goodness of segmentation was used as a benchmark for accuracy. Neocortical layer axons from a DIADEM challenge dataset were also segmented with ManSegTool and compared with the manual “gold-standard” generated for the competition.
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Affiliation(s)
- Chiara Magliaro
- Centro di Ricerca "E. Piaggio", Università di PisaPisa, Italy
| | - Alejandro L Callara
- Centro di Ricerca "E. Piaggio", Università di PisaPisa, Italy.,Dipartimento di Ingegneria dell'Informazione, Università di PisaPisa, Italy
| | - Nicola Vanello
- Centro di Ricerca "E. Piaggio", Università di PisaPisa, Italy.,Dipartimento di Ingegneria dell'Informazione, Università di PisaPisa, Italy
| | - Arti Ahluwalia
- Centro di Ricerca "E. Piaggio", Università di PisaPisa, Italy.,Dipartimento di Ingegneria dell'Informazione, Università di PisaPisa, Italy
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42
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Wang Z, Li H. Generalizing cell segmentation and quantification. BMC Bioinformatics 2017; 18:189. [PMID: 28335722 PMCID: PMC5364575 DOI: 10.1186/s12859-017-1604-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2016] [Accepted: 03/15/2017] [Indexed: 12/28/2022] Open
Abstract
Background In recent years, the microscopy technology for imaging cells has developed greatly and rapidly. The accompanying requirements for automatic segmentation and quantification of the imaged cells are becoming more and more. After studied widely in both scientific research and industrial applications for many decades, cell segmentation has achieved great progress, especially in segmenting some specific types of cells, e.g. muscle cells. However, it lacks a framework to address the cell segmentation problems generally. On the contrary, different segmentation methods were proposed to address the different types of cells, which makes the research work divergent. In addition, most of the popular segmentation and quantification tools usually require a great part of manual work. Results To make the cell segmentation work more convergent, we propose a framework that is able to segment different kinds of cells automatically and robustly in this paper. This framework evolves the previously proposed method in segmenting the muscle cells and generalizes it to be suitable for segmenting and quantifying a variety of cell images by adding more union cases. Compared to the previous methods, the segmentation and quantification accuracy of the proposed framework is also improved by three novel procedures: (1) a simplified calibration method is proposed and added for the threshold selection process; (2) a noise blob filter is proposed to get rid of the noise blobs. (3) a boundary smoothing filter is proposed to reduce the false seeds produced by the iterative erosion. As it turned out, the quantification accuracy of the proposed framework increases from 93.4 to 96.8% compared to the previous method. In addition, the accuracy of the proposed framework is also better in quantifying the muscle cells than two available state-of-the-art methods. Conclusions The proposed framework is able to automatically segment and quantify more types of cells than state-of-the-art methods. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1604-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zhenzhou Wang
- State Key Laboratory for Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China.
| | - Haixing Li
- State Key Laboratory for Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
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43
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Zandt BJ, Losnegård A, Hodneland E, Veruki ML, Lundervold A, Hartveit E. Semi-automatic 3D morphological reconstruction of neurons with densely branching morphology: Application to retinal AII amacrine cells imaged with multi-photon excitation microscopy. J Neurosci Methods 2017; 279:101-118. [PMID: 28115187 DOI: 10.1016/j.jneumeth.2017.01.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2016] [Revised: 01/10/2017] [Accepted: 01/11/2017] [Indexed: 01/30/2023]
Abstract
BACKGROUND Accurate reconstruction of the morphology of single neurons is important for morphometric studies and for developing compartmental models. However, manual morphological reconstruction can be extremely time-consuming and error-prone and algorithms for automatic reconstruction can be challenged when applied to neurons with a high density of extensively branching processes. NEW METHOD We present a procedure for semi-automatic reconstruction specifically adapted for densely branching neurons such as the AII amacrine cell found in mammalian retinas. We used whole-cell recording to fill AII amacrine cells in rat retinal slices with fluorescent dyes and acquired digital image stacks with multi-photon excitation microscopy. Our reconstruction algorithm combines elements of existing procedures, with segmentation based on adaptive thresholding and reconstruction based on a minimal spanning tree. We improved this workflow with an algorithm that reconnects neuron segments that are disconnected after adaptive thresholding, using paths extracted from the image stacks with the Fast Marching method. RESULTS By reducing the likelihood that disconnected segments were incorrectly connected to neighboring segments, our procedure generated excellent morphological reconstructions of AII amacrine cells. COMPARISON WITH EXISTING METHODS Reconstructing an AII amacrine cell required about 2h computing time, compared to 2-4days for manual reconstruction. To evaluate the performance of our method relative to manual reconstruction, we performed detailed analysis using a measure of tree structure similarity (DIADEM score), the degree of projection area overlap (Dice coefficient), and branch statistics. CONCLUSIONS We expect our procedure to be generally useful for morphological reconstruction of neurons filled with fluorescent dyes.
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Affiliation(s)
- Bas-Jan Zandt
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Are Losnegård
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | | | | | - Arvid Lundervold
- Department of Biomedicine, University of Bergen, Bergen, Norway; Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Espen Hartveit
- Department of Biomedicine, University of Bergen, Bergen, Norway.
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Zhang Z, Lim YW, Zhao P, Kanchanawong P, Motegi F. ImaEdge: a platform for the quantitative analysis of cortical proteins spatiotemporal dynamics during cell polarization. J Cell Sci 2017; 130:4200-4212. [DOI: 10.1242/jcs.206870] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Accepted: 11/01/2017] [Indexed: 11/20/2022] Open
Abstract
Cell polarity involves the compartmentalization of the cell cortex. The establishment of cortical compartments arises from the spatial bias in the activity and concentration of cortical proteins. The mechanistic dissection of cell polarity requires the accurate detection of dynamic changes in cortical proteins, but the fluctuations of cell shape and the inhomogeneous distributions of cortical proteins greatly complicate the quantitative extraction of their global and local changes during cell polarization. To address these problems, we introduce an open-source software package, ImaEdge, which automates the segmentation of the cortex from time-lapse movies, and enables quantitative extraction of cortical protein intensities. We demonstrate that ImaEdge enables efficient and rigorous analysis of the dynamic evolution of cortical PAR proteins during C. elegans embryogenesis. It is also capable of accurate tracking of varying levels of transgene expression and discontinuous signals of the actomyosin cytoskeleton during multiple rounds of cell division. ImaEdge provides a unique resource for the quantitative studies of cortical polarization, with the potential for application to many types of polarized cells.
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Affiliation(s)
- Zhen Zhang
- Mechanobiology Institute, National University of Singapore, Singapore
| | - Yen Wei Lim
- Temasek Life-sciences Laboratory, Department of Biological Sciences, National University of Singapore, Singapore
| | - Peng Zhao
- Temasek Life-sciences Laboratory, Department of Biological Sciences, National University of Singapore, Singapore
| | - Pakorn Kanchanawong
- Mechanobiology Institute, National University of Singapore, Singapore
- Department of Biomedical engineering, National University of Singapore, Singapore
| | - Fumio Motegi
- Mechanobiology Institute, National University of Singapore, Singapore
- Temasek Life-sciences Laboratory, Department of Biological Sciences, National University of Singapore, Singapore
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Abstract
Image segmentation is an important process that separates objects from the background and also from each other. Applied to cells, the results can be used for cell counting which is very important in medical diagnosis and treatment, and biological research that is often used by scientists and medical practitioners. Segmenting 3D confocal microscopy images containing cells of different shapes and sizes is still challenging as the nuclei are closely packed. The watershed transform provides an efficient tool in segmenting such nuclei provided a reasonable set of markers can be found in the image. In the presence of low-contrast variation or excessive noise in the given image, the watershed transform leads to over-segmentation (a single object is overly split into multiple objects). The traditional watershed uses the local minima of the input image and will characteristically find multiple minima in one object unless they are specified (marker-controlled watershed). An alternative to using the local minima is by a supervised technique called seeded watershed, which supplies single seeds to replace the minima for the objects. Consequently, the accuracy of a seeded watershed algorithm relies on the accuracy of the predefined seeds. In this paper, we present a segmentation approach based on the geometric morphological properties of the ‘landscape’ using curvatures. The curvatures are computed as the eigenvalues of the Shape matrix, producing accurate seeds that also inherit the original shape of their respective cells. We compare with some popular approaches and show the advantage of the proposed method.
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46
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Barbier M, Jaensch S, Cornelissen F, Vidic S, Gjerde K, de Hoogt R, Graeser R, Gustin E, Chong YT. Ellipsoid Segmentation Model for Analyzing Light-Attenuated 3D Confocal Image Stacks of Fluorescent Multi-Cellular Spheroids. PLoS One 2016; 11:e0156942. [PMID: 27303813 PMCID: PMC4909318 DOI: 10.1371/journal.pone.0156942] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2015] [Accepted: 05/23/2016] [Indexed: 02/06/2023] Open
Abstract
In oncology, two-dimensional in-vitro culture models are the standard test beds for the discovery and development of cancer treatments, but in the last decades, evidence emerged that such models have low predictive value for clinical efficacy. Therefore they are increasingly complemented by more physiologically relevant 3D models, such as spheroid micro-tumor cultures. If suitable fluorescent labels are applied, confocal 3D image stacks can characterize the structure of such volumetric cultures and, for example, cell proliferation. However, several issues hamper accurate analysis. In particular, signal attenuation within the tissue of the spheroids prevents the acquisition of a complete image for spheroids over 100 micrometers in diameter. And quantitative analysis of large 3D image data sets is challenging, creating a need for methods which can be applied to large-scale experiments and account for impeding factors. We present a robust, computationally inexpensive 2.5D method for the segmentation of spheroid cultures and for counting proliferating cells within them. The spheroids are assumed to be approximately ellipsoid in shape. They are identified from information present in the Maximum Intensity Projection (MIP) and the corresponding height view, also known as Z-buffer. It alerts the user when potential bias-introducing factors cannot be compensated for and includes a compensation for signal attenuation.
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Affiliation(s)
- Michaël Barbier
- Discovery Sciences, Janssen Pharmaceutical companies of Johnson & Johnson, Beerse, Belgium
| | - Steffen Jaensch
- Discovery Sciences, Janssen Pharmaceutical companies of Johnson & Johnson, Beerse, Belgium
| | - Frans Cornelissen
- Pharma R&D IT, Janssen Pharmaceutical companies of Johnson & Johnson, Beerse, Belgium
| | - Suzana Vidic
- Discovery Sciences, Janssen Pharmaceutical companies of Johnson & Johnson, Beerse, Belgium
- Department of Urology, Erasmus MC Rotterdam, Rotterdam, The Netherlands
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Slovenia
| | - Kjersti Gjerde
- Discovery Sciences, Janssen Pharmaceutical companies of Johnson & Johnson, Beerse, Belgium
- Department of Urology, Erasmus MC Rotterdam, Rotterdam, The Netherlands
| | - Ronald de Hoogt
- Discovery Sciences, Janssen Pharmaceutical companies of Johnson & Johnson, Beerse, Belgium
| | - Ralph Graeser
- Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim, Ingelheim am Rhein, Germany
| | - Emmanuel Gustin
- Discovery Sciences, Janssen Pharmaceutical companies of Johnson & Johnson, Beerse, Belgium
- * E-mail: (EG); (YTC)
| | - Yolanda T. Chong
- Discovery Sciences, Janssen Pharmaceutical companies of Johnson & Johnson, Beerse, Belgium
- * E-mail: (EG); (YTC)
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47
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Vinegoni C, Leon Swisher C, Fumene Feruglio P, Giedt RJ, Rousso DL, Stapleton S, Weissleder R. Real-time high dynamic range laser scanning microscopy. Nat Commun 2016; 7:11077. [PMID: 27032979 PMCID: PMC4821995 DOI: 10.1038/ncomms11077] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Accepted: 02/19/2016] [Indexed: 01/21/2023] Open
Abstract
In conventional confocal/multiphoton fluorescence microscopy, images are typically acquired under ideal settings and after extensive optimization of parameters for a given structure or feature, often resulting in information loss from other image attributes. To overcome the problem of selective data display, we developed a new method that extends the imaging dynamic range in optical microscopy and improves the signal-to-noise ratio. Here we demonstrate how real-time and sequential high dynamic range microscopy facilitates automated three-dimensional neural segmentation. We address reconstruction and segmentation performance on samples with different size, anatomy and complexity. Finally, in vivo real-time high dynamic range imaging is also demonstrated, making the technique particularly relevant for longitudinal imaging in the presence of physiological motion and/or for quantification of in vivo fast tracer kinetics during functional imaging. Confocal and multiphoton fluorescence microscopy often suffers from low dynamic range. Here the authors develop a high dynamic range, laser scanning fluorescence technique by simultaneously recording different light intensity ranges. The method can be adapted to commercial systems.
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Affiliation(s)
- C Vinegoni
- Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Richard B. Simches Research Center, 185 Cambridge Street, Boston, Massachusetts 02114, USA
| | - C Leon Swisher
- Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Richard B. Simches Research Center, 185 Cambridge Street, Boston, Massachusetts 02114, USA
| | - P Fumene Feruglio
- Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Richard B. Simches Research Center, 185 Cambridge Street, Boston, Massachusetts 02114, USA.,Department of Neurological, Biomedical and Movement Sciences, University of Verona, Strada Le Grazie 8, 37134 Verona, Italy
| | - R J Giedt
- Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Richard B. Simches Research Center, 185 Cambridge Street, Boston, Massachusetts 02114, USA
| | - D L Rousso
- Center for Brain Science, Department of Molecular and Cell Biology, Harvard University, 52 Oxford Street, Cambridge, Massachusetts 02138, USA
| | - S Stapleton
- Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Richard B. Simches Research Center, 185 Cambridge Street, Boston, Massachusetts 02114, USA
| | - R Weissleder
- Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Richard B. Simches Research Center, 185 Cambridge Street, Boston, Massachusetts 02114, USA
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48
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Hodneland E, Tai XC, Kalisch H. PDE Based Algorithms for Smooth Watersheds. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:957-966. [PMID: 26625408 DOI: 10.1109/tmi.2015.2503328] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Watershed segmentation is useful for a number of image segmentation problems with a wide range of practical applications. Traditionally, the tracking of the immersion front is done by applying a fast sorting algorithm. In this work, we explore a continuous approach based on a geometric description of the immersion front which gives rise to a partial differential equation. The main advantage of using a partial differential equation to track the immersion front is that the method becomes versatile and may easily be stabilized by introducing regularization terms. Coupling the geometric approach with a proper "merging strategy" creates a robust algorithm which minimizes over- and under-segmentation even without predefined markers. Since reliable markers defined prior to segmentation can be difficult to construct automatically for various reasons, being able to treat marker-free situations is a major advantage of the proposed method over earlier watershed formulations. The motivation for the methods developed in this paper is taken from high-throughput screening of cells. A fully automated segmentation of single cells enables the extraction of cell properties from large data sets, which can provide substantial insight into a biological model system. Applying smoothing to the boundaries can improve the accuracy in many image analysis tasks requiring a precise delineation of the plasma membrane of the cell. The proposed segmentation method is applied to real images containing fluorescently labeled cells, and the experimental results show that our implementation is robust and reliable for a variety of challenging segmentation tasks.
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49
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Bajcsy P, Cardone A, Chalfoun J, Halter M, Juba D, Kociolek M, Majurski M, Peskin A, Simon C, Simon M, Vandecreme A, Brady M. Survey statistics of automated segmentations applied to optical imaging of mammalian cells. BMC Bioinformatics 2015; 16:330. [PMID: 26472075 PMCID: PMC4608288 DOI: 10.1186/s12859-015-0762-2] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2015] [Accepted: 10/07/2015] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements. METHODS We define the scope of this survey and a classification schema first. Next, all found and manually filteredpublications are classified according to the main categories: (1) objects of interests (or objects to be segmented), (2) imaging modalities, (3) digital data axes, (4) segmentation algorithms, (5) segmentation evaluations, (6) computational hardware platforms used for segmentation acceleration, and (7) object (cellular) measurements. Finally, all classified papers are converted programmatically into a set of hyperlinked web pages with occurrence and co-occurrence statistics of assigned categories. RESULTS The survey paper presents to a reader: (a) the state-of-the-art overview of published papers about automated segmentation applied to optical microscopy imaging of mammalian cells, (b) a classification of segmentation aspects in the context of cell optical imaging, (c) histogram and co-occurrence summary statistics about cellular measurements, segmentations, segmented objects, segmentation evaluations, and the use of computational platforms for accelerating segmentation execution, and (d) open research problems to pursue. CONCLUSIONS The novel contributions of this survey paper are: (1) a new type of classification of cellular measurements and automated segmentation, (2) statistics about the published literature, and (3) a web hyperlinked interface to classification statistics of the surveyed papers at https://isg.nist.gov/deepzoomweb/resources/survey/index.html.
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Affiliation(s)
- Peter Bajcsy
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Antonio Cardone
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Joe Chalfoun
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Michael Halter
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Derek Juba
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | | | - Michael Majurski
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Adele Peskin
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Carl Simon
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Mylene Simon
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Antoine Vandecreme
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Mary Brady
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
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50
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Novel microscopy-based screening method reveals regulators of contact-dependent intercellular transfer. Sci Rep 2015; 5:12879. [PMID: 26271723 PMCID: PMC4536488 DOI: 10.1038/srep12879] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Accepted: 07/10/2015] [Indexed: 12/23/2022] Open
Abstract
Contact-dependent intercellular transfer (codeIT) of cellular constituents can have functional consequences for recipient cells, such as enhanced survival and drug resistance. Pathogenic viruses, prions and bacteria can also utilize this mechanism to spread to adjacent cells and potentially evade immune detection. However, little is known about the molecular mechanism underlying this intercellular transfer process. Here, we present a novel microscopy-based screening method to identify regulators and cargo of codeIT. Single donor cells, carrying fluorescently labelled endocytic organelles or proteins, are co-cultured with excess acceptor cells. CodeIT is quantified by confocal microscopy and image analysis in 3D, preserving spatial information. An siRNA-based screening using this method revealed the involvement of several myosins and small GTPases as codeIT regulators. Our data indicates that cellular protrusions and tubular recycling endosomes are important for codeIT. We automated image acquisition and analysis to facilitate large-scale chemical and genetic screening efforts to identify key regulators of codeIT.
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