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Morris TA, Eldeen S, Tran RDH, Grosberg A. A comprehensive review of computational and image analysis techniques for quantitative evaluation of striated muscle tissue architecture. BIOPHYSICS REVIEWS 2022; 3:041302. [PMID: 36407035 PMCID: PMC9667907 DOI: 10.1063/5.0057434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 10/03/2022] [Indexed: 06/16/2023]
Abstract
Unbiased evaluation of morphology is crucial to understanding development, mechanics, and pathology of striated muscle tissues. Indeed, the ability of striated muscles to contract and the strength of their contraction is dependent on their tissue-, cellular-, and cytoskeletal-level organization. Accordingly, the study of striated muscles often requires imaging and assessing aspects of their architecture at multiple different spatial scales. While an expert may be able to qualitatively appraise tissues, it is imperative to have robust, repeatable tools to quantify striated myocyte morphology and behavior that can be used to compare across different labs and experiments. There has been a recent effort to define the criteria used by experts to evaluate striated myocyte architecture. In this review, we will describe metrics that have been developed to summarize distinct aspects of striated muscle architecture in multiple different tissues, imaged with various modalities. Additionally, we will provide an overview of metrics and image processing software that needs to be developed. Importantly to any lab working on striated muscle platforms, characterization of striated myocyte morphology using the image processing pipelines discussed in this review can be used to quantitatively evaluate striated muscle tissues and contribute to a robust understanding of the development and mechanics of striated muscles.
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Affiliation(s)
| | - Sarah Eldeen
- Center for Complex Biological Systems, University of California, Irvine, California 92697-2700, USA
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2
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Jeong EJ, Choi D, Lee DW. U-Net Deep-Learning-Based 3D Cell Counter for the Quality Control of 3D Cell-Based Assays through Seed Cell Measurement. SLAS Technol 2021; 26:468-476. [PMID: 34078148 DOI: 10.1177/24726303211017532] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Conventional cell-counting software uses contour or watershed segmentations and focuses on identifying two-dimensional (2D) cells attached on the bottom of plastic plates. Recently developed software has been useful tools for the quality control of 2D cell-based assays by measuring initial seed cell numbers. These algorithms do not, however, quantitatively test in three-dimensional (3D) cell-based assays using extracellular matrix (ECM), because cells are aggregated and overlapped in the 3D structure of the ECM such as Matrigel, collagen, and alginate. Such overlapped and aggregated cells make it difficult to segment cells and to count the number of cells accurately. It is important, however, to determine the number of cells to standardize experiments and ensure the reproducibility of 3D cell-based assays. In this study, we apply a 3D cell-counting method using U-net deep learning to high-density aggregated cells in ECM to identify initial seed cell numbers. The proposed method showed a 10% counting error in high-density aggregated cells, while the contour and watershed segmentations showed 30% and 40% counting errors, respectively. Thus, the proposed method can reduce the seed cell-counting error in 3D cell-based assays by providing the exact number of cells to researchers, thereby enabling the acquisition of quality control in 3D cell-based assays.
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Affiliation(s)
- Eun Ji Jeong
- Department of Biomedical Engineering, Konyang University, Daejeon, Korea
| | - Donghyuk Choi
- Department of Biomedical Engineering, Konyang University, Daejeon, Korea
| | - Dong Woo Lee
- Department of Biomedical Engineering, Konyang University, Daejeon, Korea
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The Surface Crack Extraction Method Based on Machine Learning of Image and Quantitative Feature Information Acquisition Method. REMOTE SENSING 2021. [DOI: 10.3390/rs13081534] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In order to effectively control the damage caused by surface cracks to a geological environment, we need to find a convenient, efficient, and accurate method to obtain crack information. The existing crack extraction methods based on unmanned air vehicle (UAV) images inevitably have some erroneous pixels because of the complexity of background information. At the same time, there are few researches on crack feature information. In view of this, this article proposes a surface crack extraction method based on machine learning of UAV images, the data preprocessing steps, and the content and calculation methods for crack feature information: length, width, direction, location, fractal dimension, number, crack rate, and dispersion rate. The results show that the method in this article can effectively avoid the interference by vegetation and soil crust. By introducing the concept of dispersion rate, the method combining crack rate and dispersion rate can describe the distribution characteristics of regional cracks more clearly. Compared to field survey data, the calculation result of the crack feature information in this article is close to the true value, which proves that this is a reliable method for obtaining quantitative crack feature information.
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Tamashunas AC, Tocco VJ, Matthews J, Zhang Q, Atanasova KR, Paschall L, Pathak S, Ratnayake R, Stephens AD, Luesch H, Licht JD, Lele TP. High-throughput gene screen reveals modulators of nuclear shape. Mol Biol Cell 2020; 31:1392-1402. [PMID: 32320319 PMCID: PMC7353136 DOI: 10.1091/mbc.e19-09-0520] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 03/30/2020] [Accepted: 04/14/2020] [Indexed: 12/11/2022] Open
Abstract
Irregular nuclear shapes characterized by blebs, lobules, micronuclei, or invaginations are hallmarks of many cancers and human pathologies. Despite the correlation between abnormal nuclear shape and human pathologies, the mechanism by which the cancer nucleus becomes misshapen is not fully understood. Motivated by recent evidence that modifying chromatin condensation can change nuclear morphology, we conducted a high-throughput RNAi screen to identify epigenetic regulators that are required to maintain normal nuclear shape in human breast epithelial MCF-10A cells. We silenced 608 genes in parallel using an epigenetics siRNA library and used an unbiased Fourier analysis approach to quantify nuclear contour irregularity from fluorescent images captured on a high-content microscope. Using this quantitative approach, which we validated with confocal microscopy, we significantly expand the list of epigenetic regulators that impact nuclear morphology.
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Affiliation(s)
| | | | - James Matthews
- Department of Medicinal Chemistry and Center for Natural Products, Drug Discovery and Development (CNPD3), University of Florida, Gainesville, FL 32610
| | | | - Kalina R. Atanasova
- Department of Medicinal Chemistry and Center for Natural Products, Drug Discovery and Development (CNPD3), University of Florida, Gainesville, FL 32610
| | | | | | - Ranjala Ratnayake
- Department of Medicinal Chemistry and Center for Natural Products, Drug Discovery and Development (CNPD3), University of Florida, Gainesville, FL 32610
| | - Andrew D. Stephens
- Biology Department, University of Massachusetts Amherst, Amherst, MA 01003
| | - Hendrik Luesch
- Department of Medicinal Chemistry and Center for Natural Products, Drug Discovery and Development (CNPD3), University of Florida, Gainesville, FL 32610
| | - Jonathan D. Licht
- Division of Hematology/Oncology, University of Florida Health Cancer Center, Gainesville, FL 32610
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Shen Y, Kubben N, Candia J, Morozov AV, Misteli T, Losert W. RefCell: multi-dimensional analysis of image-based high-throughput screens based on 'typical cells'. BMC Bioinformatics 2018; 19:427. [PMID: 30445906 PMCID: PMC6240236 DOI: 10.1186/s12859-018-2454-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Accepted: 10/31/2018] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Image-based high-throughput screening (HTS) reveals a high level of heterogeneity in single cells and multiple cellular states may be observed within a single population. Currently available high-dimensional analysis methods are successful in characterizing cellular heterogeneity, but suffer from the "curse of dimensionality" and non-standardized outputs. RESULTS Here we introduce RefCell, a multi-dimensional analysis pipeline for image-based HTS that reproducibly captures cells with typical combinations of features in reference states and uses these "typical cells" as a reference for classification and weighting of metrics. RefCell quantitatively assesses heterogeneous deviations from typical behavior for each analyzed perturbation or sample. CONCLUSIONS We apply RefCell to the analysis of data from a high-throughput imaging screen of a library of 320 ubiquitin-targeted siRNAs selected to gain insights into the mechanisms of premature aging (progeria). RefCell yields results comparable to a more complex clustering-based single-cell analysis method; both methods reveal more potential hits than a conventional analysis based on averages.
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Affiliation(s)
- Yang Shen
- Department of Physics and Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742 USA
| | - Nard Kubben
- National Cancer Institute, National Institutes of Health, Bethesda, MD 20892 USA
| | - Julián Candia
- Trans-NIH Center for Human Immunology (CHI), National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892 USA
| | - Alexandre V. Morozov
- Department of Physics and Astronomy and Center for Quantitative Biology, Rutgers University, Piscataway, NJ 08854 USA
| | - Tom Misteli
- National Cancer Institute, National Institutes of Health, Bethesda, MD 20892 USA
| | - Wolfgang Losert
- Department of Physics and Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742 USA
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Xiong R, Verstraelen P, Demeester J, Skirtach AG, Timmermans JP, De Smedt SC, De Vos WH, Braeckmans K. Selective Labeling of Individual Neurons in Dense Cultured Networks With Nanoparticle-Enhanced Photoporation. Front Cell Neurosci 2018; 12:80. [PMID: 29651235 PMCID: PMC5884872 DOI: 10.3389/fncel.2018.00080] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Accepted: 03/07/2018] [Indexed: 12/22/2022] Open
Abstract
Neurodevelopmental and neurodegenerative disorders are characterized by subtle alterations in synaptic connections and perturbed neuronal network functionality. A hallmark of neuronal connectivity is the presence of dendritic spines, micron-sized protrusions of the dendritic shaft that compartmentalize single synapses to fine-tune synaptic strength. However, accurate quantification of spine density and morphology in mature neuronal networks is hampered by the lack of targeted labeling strategies. To resolve this, we have optimized a method to deliver cell-impermeable compounds into selected cells based on Spatially resolved NAnoparticle-enhanced Photoporation (SNAP). We show that SNAP enables efficient labeling of selected individual neurons and their spines in dense cultured networks without affecting short-term viability. We compare SNAP with widely used spine labeling techniques such as the application of lipophilic dyes and genetically encoded fluorescent markers. Using SNAP, we demonstrate a time-dependent increase in spine density in healthy cultures as well as a reduction in spine density after chemical mimicry of hypoxia. Since the sparse labeling procedure can be automated using an intelligent acquisition scheme, SNAP holds promise for high-content screening campaigns of neuronal connectivity in the context of neurodevelopmental and neurodegenerative disorders.
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Affiliation(s)
- Ranhua Xiong
- Laboratory of General Biochemistry and Physical Pharmacy, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium.,Centre for Nano- and Biophotonics, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium
| | - Peter Verstraelen
- Laboratory of Cell Biology and Histology, Department of Veterinary Sciences, University of Antwerp, Antwerp, Belgium
| | - Jo Demeester
- Laboratory of General Biochemistry and Physical Pharmacy, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium
| | - Andre G Skirtach
- Centre for Nano- and Biophotonics, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium.,Department of Molecular Biotechnology, Ghent University, Ghent, Belgium
| | - Jean-Pierre Timmermans
- Laboratory of Cell Biology and Histology, Department of Veterinary Sciences, University of Antwerp, Antwerp, Belgium
| | - Stefaan C De Smedt
- Laboratory of General Biochemistry and Physical Pharmacy, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium.,College of Chemical Engineering, Jiangsu Key Lab of Biomass-Based Green Fuels and Chemicals, Nanjing Forestry University, Nanjing, China
| | - Winnok H De Vos
- Centre for Nano- and Biophotonics, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium.,Laboratory of Cell Biology and Histology, Department of Veterinary Sciences, University of Antwerp, Antwerp, Belgium.,Department of Molecular Biotechnology, Ghent University, Ghent, Belgium
| | - Kevin Braeckmans
- Laboratory of General Biochemistry and Physical Pharmacy, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium.,Centre for Nano- and Biophotonics, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium.,Univ Lille 1, Univ Lille Nord France, IEMN, UMR 8520, Villeneuve D'Ascq, France.,Univ Lille 1, Univ Lille Nord France, Lab Phys Lasers Atomes & Mol, UMR 8523, Villeneuve D'Ascq, France
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Chirumamilla CS, Palagani A, Kamaraj B, Declerck K, Verbeek MWC, Oksana R, De Bosscher K, Bougarne N, Ruttens B, Gevaert K, Houtman R, De Vos WH, Joossens J, Van Der Veken P, Augustyns K, Van Ostade X, Bogaerts A, De Winter H, Vanden Berghe W. Selective Glucocorticoid Receptor Properties of GSK866 Analogs with Cysteine Reactive Warheads. Front Immunol 2017; 8:1324. [PMID: 29163463 PMCID: PMC5672024 DOI: 10.3389/fimmu.2017.01324] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2017] [Accepted: 09/29/2017] [Indexed: 12/17/2022] Open
Abstract
Synthetic glucocorticoids (GC) are the mainstay therapy for treatment of acute and chronic inflammatory disorders. Due to the high adverse effects associated with long-term use, GC pharmacology has focused since the nineties on more selective GC ligand-binding strategies, classified as selective glucocorticoid receptor (GR) agonists (SEGRAs) or selective glucocorticoid receptor modulators (SEGRMs). In the current study, GSK866 analogs with electrophilic covalent-binding warheads were developed with potential SEGRA properties to improve their clinical safety profile for long-lasting topical skin disease applications. Since the off-rate of a covalently binding drug is negligible compared to that of a non-covalent drug, its therapeutic effects can be prolonged and typically, smaller doses of the drug are necessary to reach the same level of therapeutic efficacy, thereby potentially reducing systemic side effects. Different analogs of SEGRA GSK866 coupled to cysteine reactive warheads were characterized for GR potency and selectivity in various biochemical and cellular assays. GR- and NFκB-dependent reporter gene studies show favorable anti-inflammatory properties with reduced GR transactivation of two non-steroidal GSK866 analogs UAMC-1217 and UAMC-1218, whereas UAMC-1158 and UAMC-1159 compounds failed to modulate cellular GR activity. These results were further supported by GR immuno-localization and S211 phospho-GR western analysis, illustrating significant GR phosphoactivation and nuclear translocation upon treatment of GSK866, UAMC-1217, or UAMC-1218, but not in case of UAMC-1158 or UAMC-1159. Furthermore, mass spectrometry analysis of tryptic peptides of recombinant GR ligand-binding domain (LBD) bound to UAMC-1217 or UAMC-1218 confirmed covalent cysteine-dependent GR binding. Finally, molecular dynamics simulations, as well as glucocorticoid receptor ligand-binding domain (GR-LBD) coregulator interaction profiling of the GR-LBD bound to GSK866 or its covalently binding analogs UAMC-1217 or UAMC-1218 revealed subtle conformational differences that might underlie their SEGRA properties. Altogether, GSK866 analogs UAMC-1217 and UAMC-1218 hold promise as a novel class of covalent-binding SEGRA ligands for the treatment of topical inflammatory skin disorders.
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Affiliation(s)
- Chandra S Chirumamilla
- Laboratory of Protein Chemistry, Proteomics and Epigenetic Signalling, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Ajay Palagani
- Laboratory of Protein Chemistry, Proteomics and Epigenetic Signalling, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Balu Kamaraj
- Research Group PLASMANT, Department of Chemistry, University of Antwerp, Antwerp, Belgium
| | - Ken Declerck
- Laboratory of Protein Chemistry, Proteomics and Epigenetic Signalling, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Marinus W C Verbeek
- Laboratory of Protein Chemistry, Proteomics and Epigenetic Signalling, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Ryabtsova Oksana
- Medicinal Chemistry, Department of Pharmaceutical Sciences, University of Antwerp, Antwerp, Belgium
| | - Karolien De Bosscher
- Receptor Research Laboratories, Nuclear Receptor Lab (NRL) and Cytokine Receptor Lab (CRL), VIB-UGent Center for Medical Biotechnology, Ghent University, Ghent, Belgium
| | - Nadia Bougarne
- Receptor Research Laboratories, Nuclear Receptor Lab (NRL) and Cytokine Receptor Lab (CRL), VIB-UGent Center for Medical Biotechnology, Ghent University, Ghent, Belgium
| | - Bart Ruttens
- Center for Medical Biotechnology, Department of Biochemistry, VIB, Ghent University, Ghent, Belgium
| | - Kris Gevaert
- Center for Medical Biotechnology, Department of Biochemistry, VIB, Ghent University, Ghent, Belgium
| | - René Houtman
- PamGene International B.V., Den Bosch, Netherlands
| | - Winnok H De Vos
- Laboratory of Cell Biology and Histology, Department of Veterinary Sciences, University of Antwerp, Antwerp, Belgium
| | - Jurgen Joossens
- Medicinal Chemistry, Department of Pharmaceutical Sciences, University of Antwerp, Antwerp, Belgium
| | - Pieter Van Der Veken
- Medicinal Chemistry, Department of Pharmaceutical Sciences, University of Antwerp, Antwerp, Belgium
| | - Koen Augustyns
- Medicinal Chemistry, Department of Pharmaceutical Sciences, University of Antwerp, Antwerp, Belgium
| | - Xaveer Van Ostade
- Laboratory of Protein Chemistry, Proteomics and Epigenetic Signalling, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Annemie Bogaerts
- Research Group PLASMANT, Department of Chemistry, University of Antwerp, Antwerp, Belgium
| | - Hans De Winter
- Medicinal Chemistry, Department of Pharmaceutical Sciences, University of Antwerp, Antwerp, Belgium
| | - Wim Vanden Berghe
- Laboratory of Protein Chemistry, Proteomics and Epigenetic Signalling, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
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Correction: Accurate Detection of Dysmorphic Nuclei Using Dynamic Programming and Supervised Classification. PLoS One 2017; 12:e0173701. [PMID: 28264070 PMCID: PMC5338863 DOI: 10.1371/journal.pone.0173701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
[This corrects the article DOI: 10.1371/journal.pone.0170688.].
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