1
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Keikhosravi A, Almansour F, Bohrer CH, Fursova NA, Guin K, Sood V, Misteli T, Larson DR, Pegoraro G. High-throughput image processing software for the study of nuclear architecture and gene expression. Sci Rep 2024; 14:18426. [PMID: 39117696 PMCID: PMC11310328 DOI: 10.1038/s41598-024-66600-1] [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: 10/27/2023] [Accepted: 07/02/2024] [Indexed: 08/10/2024] Open
Abstract
High-throughput imaging (HTI) generates complex imaging datasets from a large number of experimental perturbations. Commercial HTI software programs for image analysis workflows typically do not allow full customization and adoption of new image processing algorithms in the analysis modules. While open-source HTI analysis platforms provide individual modules in the workflow, like nuclei segmentation, spot detection, or cell tracking, they are often limited in integrating novel analysis modules or algorithms. Here, we introduce the High-Throughput Image Processing Software (HiTIPS) to expand the range and customization of existing HTI analysis capabilities. HiTIPS incorporates advanced image processing and machine learning algorithms for automated cell and nuclei segmentation, spot signal detection, nucleus tracking, nucleus registration, spot tracking, and quantification of spot signal intensity. Furthermore, HiTIPS features a graphical user interface that is open to integration of new analysis modules for existing analysis pipelines and to adding new analysis modules. To demonstrate the utility of HiTIPS, we present three examples of image analysis workflows for high-throughput DNA FISH, immunofluorescence (IF), and live-cell imaging of transcription in single cells. Altogether, we demonstrate that HiTIPS is a user-friendly, flexible, and open-source HTI software platform for a variety of cell biology applications.
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Affiliation(s)
- Adib Keikhosravi
- High-Throughput Imaging Facility, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Faisal Almansour
- Cell Biology of Genomes, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
- Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical School, Washington, DC, 20057, USA
| | - Christopher H Bohrer
- System Biology of Gene Expression, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Nadezda A Fursova
- System Biology of Gene Expression, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Krishnendu Guin
- Cell Biology of Genomes, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Varun Sood
- Cell Biology of Genomes, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
- System Biology of Gene Expression, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Tom Misteli
- Cell Biology of Genomes, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Daniel R Larson
- System Biology of Gene Expression, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Gianluca Pegoraro
- High-Throughput Imaging Facility, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA.
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2
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Carreras-Puigvert J, Spjuth O. Artificial intelligence for high content imaging in drug discovery. Curr Opin Struct Biol 2024; 87:102842. [PMID: 38797109 DOI: 10.1016/j.sbi.2024.102842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 04/28/2024] [Accepted: 04/29/2024] [Indexed: 05/29/2024]
Abstract
Artificial intelligence (AI) and high-content imaging (HCI) are contributing to advancements in drug discovery, propelled by the recent progress in deep neural networks. This review highlights AI's role in analysis of HCI data from fixed and live-cell imaging, enabling novel label-free and multi-channel fluorescent screening methods, and improving compound profiling. HCI experiments are rapid and cost-effective, facilitating large data set accumulation for AI model training. However, the success of AI in drug discovery also depends on high-quality data, reproducible experiments, and robust validation to ensure model performance. Despite challenges like the need for annotated compounds and managing vast image data, AI's potential in phenotypic screening and drug profiling is significant. Future improvements in AI, including increased interpretability and integration of multiple modalities, are expected to solidify AI and HCI's role in drug discovery.
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Affiliation(s)
- Jordi Carreras-Puigvert
- Department of Pharmaceutical Biosciences and Science for Life Laboratories, Uppsala University, Sweden.
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences and Science for Life Laboratories, Uppsala University, Sweden.
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3
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Ram AS, Matuszewska K, McKenna C, Petrik J, Oblak ML. Validation of a semi-quantitative scoring system and workflow for analysis of fluorescence quantification in companion animals. Front Vet Sci 2024; 11:1392504. [PMID: 39144083 PMCID: PMC11322124 DOI: 10.3389/fvets.2024.1392504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 07/11/2024] [Indexed: 08/16/2024] Open
Abstract
Significance Many commercially available near-infrared (NIR) fluorescence imaging systems lack algorithms for real-time quantifiable fluorescence data. Creation of a workflow for clinical assessment and post hoc analysis may provide clinical researchers with a method for intraoperative fluorescence quantification to improve objective outcome measures. Aim Scoring systems and verified image analysis are employed to determine the amount and intensity of fluorescence within surgical specimens both intra and postoperatively. Approach Lymph nodes from canine cancer patients were obtained during lymph node extirpation following peritumoral injection of indocyanine green (ICG). First, a semi-quantitative assessment of surface fluorescence was evaluated. Images obtained with a NIR exoscope were analysed to determine fluorescence thresholds and measure fluorescence amount and intensity. Results Post hoc fluorescence quantification (threshold of Hue = 165-180, Intensity = 30-255) displayed strong agreement with semi-quantitative scoring (k = 0.9734, p < 0.0001). Fluorescence intensity with either threshold of 35-255 or 45-255 were significant predictors of fluorescence and had high sensitivity and specificity (p < 0.05). Fluorescence intensity and quantification had a strong association (p < 0.001). Conclusion The validation of the semi-quantitative scoring system by image analysis provides a method for objective in situ observation of tissue fluorescence. The utilization of thresholding for ICG fluorescence intensity allows post hoc quantification of fluorescence when not built into the imaging system.
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Affiliation(s)
- Ann S. Ram
- Department of Biomedical Sciences, University of Guelph, Guelph, ON, Canada
| | - Kathy Matuszewska
- Department of Biomedical Sciences, University of Guelph, Guelph, ON, Canada
| | - Charly McKenna
- Department of Clinical Studies, University of Guelph, Guelph, ON, Canada
| | - Jim Petrik
- Department of Biomedical Sciences, University of Guelph, Guelph, ON, Canada
| | - Michelle L. Oblak
- Department of Clinical Studies, University of Guelph, Guelph, ON, Canada
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4
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Keikhosravi A, Almansour F, Bohrer CH, Fursova NA, Guin K, Sood V, Misteli T, Larson DR, Pegoraro G. HiTIPS: High-Throughput Image Processing Software for the Study of Nuclear Architecture and Gene Expression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.02.565366. [PMID: 38076967 PMCID: PMC10705580 DOI: 10.1101/2023.11.02.565366] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/14/2024]
Abstract
High-throughput imaging (HTI) generates complex imaging datasets from a large number of experimental perturbations. Commercial HTI software for image analysis workflows does not allow full customization and adoption of new image processing algorithms in the analysis modules. While open-source HTI analysis platforms provide individual modules in the workflow, like nuclei segmentation, spot detection, or cell tracking, they are often limited in integrating novel analysis modules or algorithms. Here, we introduce the High-Throughput Image Processing Software (HiTIPS) to expand the range and customization of existing HTI analysis capabilities. HiTIPS incorporates advanced image processing and machine learning algorithms for automated cell and nuclei segmentation, spot signal detection, nucleus tracking, spot tracking, and quantification of spot signal intensity. Furthermore, HiTIPS features a graphical user interface that is open to integration of new algorithms for existing analysis pipelines and to adding new analysis pipelines through separate plugins. To demonstrate the utility of HiTIPS, we present three examples of image analysis workflows for high-throughput DNA FISH, immunofluorescence (IF), and live-cell imaging of transcription in single cells. Altogether, we demonstrate that HiTIPS is a user-friendly, flexible, and open-source HTI analysis platform for a variety of cell biology applications.
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5
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Silberberg M, Grecco HE. Robust and unbiased estimation of the background distribution for automated quantitative imaging. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2023; 40:C8-C15. [PMID: 37132946 DOI: 10.1364/josaa.477468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Background estimation is the first step in quantitative analysis of images. It has an impact on all subsequent analyses, in particular for segmentation and calculation of ratiometric quantities. Most methods recover only a single value such as the median or yield a biased estimation in non-trivial cases. We introduce, to our knowledge, the first method to recover an unbiased estimation of background distribution. It leverages the lack of local spatial correlation in background pixels to robustly select a subset that accurately represents the background. The resulting background distribution can be used to test for foreground membership of individual pixels or estimate confidence intervals in derived quantities.
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6
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Summers HD, Wills JW, Rees P. Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis. CELL REPORTS METHODS 2022; 2:100348. [PMID: 36452868 PMCID: PMC9701617 DOI: 10.1016/j.crmeth.2022.100348] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Automated microscopy and computational image analysis has transformed cell biology, providing quantitative, spatially resolved information on cells and their constituent molecules from the sub-micron to the whole-organ scale. Here we explore the application of spatial statistics to the cellular relationships within tissue microscopy data and discuss how spatial statistics offers cytometry a powerful yet underused mathematical tool set for which the required data are readily captured using standard protocols and microscopy equipment. We also highlight the often-overlooked need to carefully consider the structural heterogeneity of tissues in terms of the applicability of different statistical measures and their accuracy and demonstrate how spatial analyses offer a great deal more than just basic quantification of biological variance. Ultimately, we highlight how statistical modeling can help reveal the hierarchical spatial processes that connect the properties of individual cells to the establishment of biological function.
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Affiliation(s)
- Huw D. Summers
- Department of Biomedical Engineering, Swansea University, Swansea SA1 8QQ, UK
| | - John W. Wills
- Department of Veterinary Medicine, University of Cambridge, Cambridge CB3 0ES, UK
| | - Paul Rees
- Department of Biomedical Engineering, Swansea University, Swansea SA1 8QQ, UK
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7
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Halsted MC, Bible AN, Morrell-Falvey JL, Retterer ST. Quantifying biofilm propagation on chemically modified surfaces. Biofilm 2022; 4:100088. [PMID: 36303845 PMCID: PMC9594113 DOI: 10.1016/j.bioflm.2022.100088] [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: 06/08/2022] [Revised: 09/26/2022] [Accepted: 10/14/2022] [Indexed: 11/07/2022] Open
Abstract
Conditions affecting biofilm formation differ among bacterial species and this presents a challenge to studying biofilms in the lab. This work leverages functionalized silanes to control surface chemistry in the study of early biofilm propagation, quantified with a semi-automated image processing algorithm. These methods support the study of Pantoea sp. YR343, a gram-negative bacterium isolated from the poplar rhizosphere. We found that Pantoea sp. YR343 does not readily attach to hydrophilic surfaces but will form biofilms with a “honeycomb” morphology on hydrophobic surfaces. Our image processing algorithm described here quantified the evolution of the honeycomb morphology over time, and found the propagation to display a logarithmic behavior. This methodology was repeated with a flagella-deficient fliR mutant of Pantoea sp. YR343 which resulted in reduced surface attachment. Quantifiable differences between Pantoea WT and ΔfliR biofilm morphologies were captured by the image processing algorithm, further demonstrating the insight gained from these methods.
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Affiliation(s)
| | - Amber N. Bible
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | | | - Scott T. Retterer
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA,Center for Nanophase Materials Sciences, Oak Ridge, TN, USA,Corresponding author. Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA.
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8
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Li D, Wang G, Werner R, Xie H, Guan JS, Hilgetag CC. Single Image-Based Vignetting Correction for Improving the Consistency of Neural Activity Analysis in 2-Photon Functional Microscopy. Front Neuroinform 2022; 15:674439. [PMID: 35069164 PMCID: PMC8766855 DOI: 10.3389/fninf.2021.674439] [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] [Received: 03/01/2021] [Accepted: 12/01/2021] [Indexed: 12/04/2022] Open
Abstract
High-resolution functional 2-photon microscopy of neural activity is a cornerstone technique in current neuroscience, enabling, for instance, the image-based analysis of relations of the organization of local neuron populations and their temporal neural activity patterns. Interpreting local image intensity as a direct quantitative measure of neural activity presumes, however, a consistent within- and across-image relationship between the image intensity and neural activity, which may be subject to interference by illumination artifacts. In particular, the so-called vignetting artifact—the decrease of image intensity toward the edges of an image—is, at the moment, widely neglected in the context of functional microscopy analyses of neural activity, but potentially introduces a substantial center-periphery bias of derived functional measures. In the present report, we propose a straightforward protocol for single image-based vignetting correction. Using immediate-early gene-based 2-photon microscopic neural image data of the mouse brain, we show the necessity of correcting both image brightness and contrast to improve within- and across-image intensity consistency and demonstrate the plausibility of the resulting functional data.
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Affiliation(s)
- Dong Li
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- *Correspondence: Dong Li,
| | - Guangyu Wang
- School of Life Sciences and Technology, ShanghaiTech University, Shanghai, China
| | - René Werner
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Hong Xie
- Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Ji-Song Guan
- School of Life Sciences and Technology, ShanghaiTech University, Shanghai, China
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Claus C. Hilgetag
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Health Sciences, Boston University, Boston, MA, United States
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9
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Leavesley SJ, Annamdevula N, Johnson S, Pleshinger DJ, Rich TC. Automated Image Analysis of FRET Signals for Subcellular cAMP Quantification. Methods Mol Biol 2022; 2483:167-180. [PMID: 35286675 DOI: 10.1007/978-1-0716-2245-2_10] [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: 06/14/2023]
Abstract
A variety of FRET probes have been developed to examine cAMP localization and dynamics in single cells. These probes offer a readily accessible approach to measure localized cAMP signals. However, given the low signal-to-noise ratio of most FRET probes and the dynamic nature of the intracellular environment, there have been marked limitations in the ability to use FRET probes to study localized signaling events within the same cell. Here, we outline a methodology to dissect kinetics of cAMP-mediated FRET signals in single cells using automated image analysis approaches. We additionally extend these approaches to the analysis of subcellular regions. These approaches offer a unique opportunity to assess localized cAMP kinetics in an unbiased, quantitative fashion.
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Affiliation(s)
- Silas J Leavesley
- Department of Chemical and Biomolecular Engineering, University of South Alabama, Mobile, AL, USA.
- Department of Pharmacology, University of South Alabama, Mobile, AL, USA.
- Center for Lung Biology, University of South Alabama, Mobile, AL, USA.
| | - Naga Annamdevula
- Department of Pharmacology, University of South Alabama, Mobile, AL, USA
- Center for Lung Biology, University of South Alabama, Mobile, AL, USA
- Department of Physiology, University of South Alabama, Mobile, AL, USA
| | - Santina Johnson
- Department of Pharmacology, University of South Alabama, Mobile, AL, USA
- Center for Lung Biology, University of South Alabama, Mobile, AL, USA
| | - D J Pleshinger
- Department of Pharmacology, University of South Alabama, Mobile, AL, USA
- Center for Lung Biology, University of South Alabama, Mobile, AL, USA
| | - Thomas C Rich
- Department of Pharmacology, University of South Alabama, Mobile, AL, USA
- Center for Lung Biology, University of South Alabama, Mobile, AL, USA
- Department of Physiology, University of South Alabama, Mobile, AL, USA
- College of Engineering, University of South Alabama, Mobile, AL, USA
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10
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Summers HD. Practical machine learning for disease diagnosis. CELL REPORTS METHODS 2021; 1:100103. [PMID: 35474900 PMCID: PMC9017117 DOI: 10.1016/j.crmeth.2021.100103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Deep learning neural networks are a powerful tool in the analytical toolbox of modern microscopy, but they come with an exacting requirement for accurately annotated, ground truth cell images. Otesteanu et al. (2021) elegantly streamline this process, implementing network training by using patient-level rather than cell-level disease classification.
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Affiliation(s)
- Huw D. Summers
- Department of Biomedical Engineering, Swansea University, Swansea, UK
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11
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Rahmanuddin S, Korn R, Cridebring D, Borazanci E, Brase J, Boswell W, Jamil A, Cai W, Sabir A, Motarjem P, Koay E, Mitra A, Goel A, Ho J, Chung V, Von Hoff DD. Role of 3D Volumetric and Perfusion Imaging for Detecting Early Changes in Pancreatic Adenocarcinoma. Front Oncol 2021; 11:678617. [PMID: 34568010 PMCID: PMC8456995 DOI: 10.3389/fonc.2021.678617] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 08/13/2021] [Indexed: 12/30/2022] Open
Abstract
Purpose There is a major shortage of reliable early detection methods for pancreatic cancer in high-risk groups. The focus of this preliminary study was to use Time Intensity-Density Curve (TIDC) and Marley Equation analyses, in conjunction with 3D volumetric and perfusion imaging to demonstrate their potential as imaging biomarkers to assist in the early detection of Pancreatic Ductal Adenocarcinoma (PDAC). Experimental Designs A quantitative retrospective and prospective study was done by analyzing multi-phase Computed Tomography (CT) images of 28 patients undergoing treatment at different stages of pancreatic adenocarcinoma using advanced 3D imaging software to identify the perfusion and radio density of tumors. Results TIDC and the Marley Equation proved useful in quantifying tumor aggressiveness. Perfusion delays in the venous phase can be linked to Vascular Endothelial Growth Factor (VEGF)-related activity which represents the active part of the tumor. 3D volume analysis of the multiphase CT scan of the patient showed clear changes in arterial and venous perfusion indicating the aggressive state of the tumor. Conclusion TIDC and 3D volumetric analysis can play a significant role in defining the response of the tumor to treatment and identifying early-stage aggressiveness.
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Affiliation(s)
- Syed Rahmanuddin
- National Medical Center & Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, United States
| | - Ronald Korn
- Virginia G Piper Cancer Center, Honor Health, Scottsdale, AZ, United States
| | - Derek Cridebring
- Molecular Medicine Division, Translational Genomics Research Institute (TGEN), Phoenix, AZ, United States
| | - Erkut Borazanci
- Virginia G Piper Cancer Center, Honor Health, Scottsdale, AZ, United States
| | - Jordyn Brase
- National Medical Center & Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, United States
| | - William Boswell
- National Medical Center & Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, United States
| | - Asma Jamil
- National Medical Center & Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, United States
| | - Wenli Cai
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Aqsa Sabir
- National Medical Center & Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, United States
| | - Pejman Motarjem
- National Medical Center & Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, United States
| | - Eugene Koay
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Anirban Mitra
- Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Ajay Goel
- Molecular Diagnostic and Experimental Therapeutics, City of Hope Comprehensive Cancer Center, Monrovia, CA, United States
| | - Joyce Ho
- Molecular Diagnostic and Experimental Therapeutics, City of Hope Comprehensive Cancer Center, Monrovia, CA, United States
| | - Vincent Chung
- Molecular Diagnostic and Experimental Therapeutics, City of Hope Comprehensive Cancer Center, Monrovia, CA, United States
| | - Daniel D Von Hoff
- National Medical Center & Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, United States.,Virginia G Piper Cancer Center, Honor Health, Scottsdale, AZ, United States.,Molecular Medicine Division, Translational Genomics Research Institute (TGEN), Phoenix, AZ, United States
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12
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Mehrabi M, Morris TA, Cang Z, Nguyen CHH, Sha Y, Asad MN, Khachikyan N, Greene TL, Becker DM, Nie Q, Zaragoza MV, Grosberg A. A Study of Gene Expression, Structure, and Contractility of iPSC-Derived Cardiac Myocytes from a Family with Heart Disease due to LMNA Mutation. Ann Biomed Eng 2021; 49:3524-3539. [PMID: 34585335 PMCID: PMC8671287 DOI: 10.1007/s10439-021-02850-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 08/06/2021] [Indexed: 12/18/2022]
Abstract
Genetic mutations to the Lamin A/C gene (LMNA) can cause heart disease, but the mechanisms making cardiac tissues uniquely vulnerable to the mutations remain largely unknown. Further, patients with LMNA mutations have highly variable presentation of heart disease progression and type. In vitro patient-specific experiments could provide a powerful platform for studying this phenomenon, but the use of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CM) introduces heterogeneity in maturity and function thus complicating the interpretation of the results of any single experiment. We hypothesized that integrating single cell RNA sequencing (scRNA-seq) with analysis of the tissue architecture and contractile function would elucidate some of the probable mechanisms. To test this, we investigated five iPSC-CM lines, three controls and two patients with a (c.357-2A>G) mutation. The patient iPSC-CM tissues had significantly weaker stress generation potential than control iPSC-CM tissues demonstrating the viability of our in vitro approach. Through scRNA-seq, differentially expressed genes between control and patient lines were identified. Some of these genes, linked to quantitative structural and functional changes, were cardiac specific, explaining the targeted nature of the disease progression seen in patients. The results of this work demonstrate the utility of combining in vitro tools in exploring heart disease mechanics.
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Affiliation(s)
- Mehrsa Mehrabi
- Department of Biomedical Engineering, University of California, Irvine, CA, 92697, USA.,UCI Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center (CIRC), University of California, Irvine, CA, 92697, USA
| | - Tessa A Morris
- UCI Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center (CIRC), University of California, Irvine, CA, 92697, USA.,Center for Complex Biological Systems, University of California, Irvine, CA, 92697, USA
| | - Zixuan Cang
- Department of Mathematics and Developmental & Cell Biology, University of California, Irvine, CA, 92697, USA.,The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA, 92697, USA
| | - Cecilia H H Nguyen
- Genetics & Genomics Division, Department of Pediatrics, School of Medicine, University of California, Irvine, CA, 92697, USA
| | - Yutong Sha
- Department of Mathematics and Developmental & Cell Biology, University of California, Irvine, CA, 92697, USA
| | - Mira N Asad
- Department of Biomedical Engineering, University of California, Irvine, CA, 92697, USA.,UCI Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center (CIRC), University of California, Irvine, CA, 92697, USA
| | - Nyree Khachikyan
- Department of Biomedical Engineering, University of California, Irvine, CA, 92697, USA.,UCI Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center (CIRC), University of California, Irvine, CA, 92697, USA
| | - Taylor L Greene
- Department of Biomedical Engineering, University of California, Irvine, CA, 92697, USA.,UCI Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center (CIRC), University of California, Irvine, CA, 92697, USA
| | - Danielle M Becker
- Department of Biomedical Engineering, University of California, Irvine, CA, 92697, USA.,UCI Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center (CIRC), University of California, Irvine, CA, 92697, USA
| | - Qing Nie
- Department of Biomedical Engineering, University of California, Irvine, CA, 92697, USA.,Department of Mathematics and Developmental & Cell Biology, University of California, Irvine, CA, 92697, USA.,The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA, 92697, USA
| | - Michael V Zaragoza
- Department of Biological Chemistry, School of Medicine, University of California, Irvine, CA, 92697, USA.,Genetics & Genomics Division, Department of Pediatrics, School of Medicine, University of California, Irvine, CA, 92697, USA
| | - Anna Grosberg
- Department of Biomedical Engineering, University of California, Irvine, CA, 92697, USA. .,UCI Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center (CIRC), University of California, Irvine, CA, 92697, USA. .,Center for Complex Biological Systems, University of California, Irvine, CA, 92697, USA. .,The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA, 92697, USA. .,Department of Chemical and Biomolecular Engineering, University of California, Irvine, CA, 92697, USA. .,The Henry Samueli School of Engineering, University of California, Irvine, 2418 Engineering Hall, Irvine, CA, 92697, USA.
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13
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Wang J, Wang X, Zhang P, Xie S, Fu S, Li Y, Han H. Correction of uneven illumination in color microscopic image based on fully convolutional network. OPTICS EXPRESS 2021; 29:28503-28520. [PMID: 34614979 DOI: 10.1364/oe.433064] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 07/27/2021] [Indexed: 06/13/2023]
Abstract
The correction of uneven illumination in microscopic image is a basic task in medical imaging. Most of the existing methods are designed for monochrome images. An effective fully convolutional network (FCN) is proposed to directly process color microscopic image in this paper. The proposed method estimates the distribution of illumination information in input image, and then carry out the correction of the corresponding uneven illumination through a feature encoder module, a feature decoder module, and a detail supplement module. In this process, overlapping residual blocks are designed to better transfer the illumination information, and in particular a well-designed weighted loss function ensures that the network can not only correct the illumination but also preserve image details. The proposed method is compared with some related methods on real pathological cell images qualitatively and quantitatively. Experimental results show that our method achieves the excellent performance. The proposed method is also applied to the preprocessing of whole slide imaging (WSI) tiles, which greatly improves the effect of image mosaicking.
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14
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Qiao T, Kim S, Lee W, Lee H. An enhanced fluorescence detection of a nitroaromatic compound using bacteria embedded in porous poly lactic-co-glycolic acid microbeads. Analyst 2021; 146:4615-4621. [PMID: 34164639 DOI: 10.1039/d1an00510c] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The detection of explosive nitroaromatic compounds has caused worldwide concern for human safety. In this study, we introduce a fluorescent biosensor based on porous biocompatible microspheres loaded with a bioreporter for the detection of nitroaromatic compounds. Poly(lactic-co-glycolic acid) microbeads were designed as biosensors embedded with the bacterial bioreporters. The genetically engineered bacterial bioreporter can express a green fluorescent protein in response to nitroaromatic compounds (e.g., trinitrotoluene and dinitrotoluene). The modified surface structure in microbeads provides a large surface area, as well as easy penetration, and increases the number of attached bioreporters for enhanced fluorescent signals of biosensors. Moreover, the addition of the M13 bacteriophage in open porous microbeads significantly amplified the fluorescence signal for detection by the π-π interaction between peptides in the M13 bacteriophage and nitroaromatic compounds. The modification of the surface morphology, as well as the genetically engineered M13 phage, significantly amplifies the fluorescence signal, which makes the detection of explosives easier, and has great potential for the stand-off remote sensing of TNT buried in the field.
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Affiliation(s)
- Tian Qiao
- Department of Materials Science and Engineering, Kookmin Univ.77 Jeongneung-ro, Seongbuk-gu, Seoul, 02707, Republic of Korea.
| | - Soohyun Kim
- Department of Materials Science and Engineering, Kookmin Univ.77 Jeongneung-ro, Seongbuk-gu, Seoul, 02707, Republic of Korea.
| | - Wonmok Lee
- Department of Chemistry, Sejong Univ., Neungdong-ro 209, Gwangjin-gu, Seoul, 143747, Republic of Korea.
| | - Hyunjung Lee
- Department of Materials Science and Engineering, Kookmin Univ.77 Jeongneung-ro, Seongbuk-gu, Seoul, 02707, Republic of Korea.
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15
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Pringle NA, van de Venter M, Koekemoer TC. Comprehensive in vitro antidiabetic screening of Aspalathus linearis using a target-directed screening platform and cellomics. Food Funct 2021; 12:1020-1038. [PMID: 33416070 DOI: 10.1039/d0fo02611e] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The antidiabetic potential of Aspalathus linearis has been investigated for over a decade, however, its characterisation remains incomplete with results scattered across numerous journals making the information difficult to compare and integrate. To explore whether any potential antidiabetic mechanisms for A. linearis have been neglected and to compare the suitability of extracts of green and "fermented" A. linearis as potential antidiabetic treatment strategies, this study utilised a comprehensive in vitro antidiabetic target-directed screening platform in combination with high content screening and analysis/cellomics. The antidiabetic screening platform consisted of 20 different screening assays that incorporated 5 well-characterised antidiabetic targets i.e. the intestine, liver, skeletal muscle, adipose tissue/obesity and pancreatic β-cells. Both the green and fermented extracts of A. linearis demonstrated very broad antidiabetic mechanisms as they revealed several promising activities that could be useful in combatting insulin resistance, inflammation, oxidative stress, protein glycation and pancreatic β-cell dysfunction and death - with a strong tendency to attenuate postprandial hyperglycaemia and the subsequent metabolic dysfunction which arises as a result of poor glycaemic control. The green extract was more successful at combatting oxidative stress in INS-1 pancreatic β-cells and enhancing intracellular calcium levels in the absence of glucose. Conversely, the fermented extract demonstrated a greater ability to inhibit α-glucosidase activity as well as palmitic acid-induced free fatty acid accumulation in C3A hepatocytes and differentiated L6 myotubes, however, further studies are required to clarify the potentially toxic and pro-inflammatory nature of the fermented extract.
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Affiliation(s)
- Nadine A Pringle
- Department of Biochemistry and Microbiology, Nelson Mandela University, Port Elizabeth, South Africa.
| | - Maryna van de Venter
- Department of Biochemistry and Microbiology, Nelson Mandela University, Port Elizabeth, South Africa.
| | - Trevor C Koekemoer
- Department of Biochemistry and Microbiology, Nelson Mandela University, Port Elizabeth, South Africa.
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16
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Veschini L, Sailem H, Malani D, Pietiäinen V, Stojiljkovic A, Wiseman E, Danovi D. High-Content Imaging to Phenotype Human Primary and iPSC-Derived Cells. Methods Mol Biol 2021; 2185:423-445. [PMID: 33165865 DOI: 10.1007/978-1-0716-0810-4_27] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Increasingly powerful microscopy, liquid handling, and computational techniques have enabled cell imaging in high throughput. Microscopy images are quantified using high-content analysis platforms linking object features to cell behavior. This can be attempted on physiologically relevant cell models, including stem cells and primary cells, in complex environments, and conceivably in the presence of perturbations. Recently, substantial focus has been devoted to cell profiling for cell therapy, assays for drug discovery or biomarker identification for clinical decision-making protocols, bringing this wealth of information into translational applications. In this chapter, we focus on two protocols enabling to (1) benchmark human cells, in particular human endothelial cells as a case study and (2) extract cells from blood for follow-up experiments including image-based drug testing. We also present concepts of high-content imaging and discuss the benefits and challenges, with the aim of enabling readers to tailor existing pipelines and bring such approaches closer to translational research and the clinic.
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Affiliation(s)
- Lorenzo Veschini
- Academic Centre of Reconstructive Science, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK
| | - Heba Sailem
- The Institute of Biomedical Engineering, Oxford, UK
| | - Disha Malani
- Institute for Molecular Medicine Finland-FIMM, Helsinki Institute of Life Science-HiLIFE, University of Helsinki, Helsinki, Finland
| | - Vilja Pietiäinen
- Institute for Molecular Medicine Finland-FIMM, Helsinki Institute of Life Science-HiLIFE, University of Helsinki, Helsinki, Finland
| | - Ana Stojiljkovic
- Division of Veterinary Anatomy, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Erika Wiseman
- Stem Cell Hotel, Centre for Stem Cells and Regenerative Medicine, King's College London, London, UK
| | - Davide Danovi
- Stem Cell Hotel, Centre for Stem Cells and Regenerative Medicine, King's College London, London, UK.
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17
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Legner M, Jonkman J, Swift D. Evaluating the Effects of Disinfectants on Bacterial Biofilms Using a Microfluidics Flow Cell and Time-Lapse Fluorescence Microscopy. Microorganisms 2020; 8:microorganisms8111837. [PMID: 33266442 PMCID: PMC7700140 DOI: 10.3390/microorganisms8111837] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 11/11/2020] [Accepted: 11/13/2020] [Indexed: 12/03/2022] Open
Abstract
A commercially available microfluidics flow cell was utilized together with widefield fluorescence microscopy to evaluate the effects of disinfectants on bacterial strains. The flow cell’s inner surface supports the formation of biofilms of numerous bacterial species. The modular setup of the flow cell accessories allows connection to syringes, pumps and collection vials, facilitating aseptic experiments in a controlled fluidics environment which can be documented with precisely timed microscopy imaging. The flow cell is inoculated with a suspension of bacteria in a nutrient medium and incubated for several days allowing bacterial cells to form a biofilm. Shortly before performing an assay, the biofilm is labelled with a dual-fluorescent DNA probe which distinguishes unharmed and damaged bacteria. Then a disinfectant sample (or control) is gently injected and time-lapse imaging is used for quantifying the course of bacterial biomass response. We use a simplified widefield microscopy method that allows intensive recording and quantification of time series of two-dimensional frames for tracking the course of disinfectant action on a variety of microbial strains. This procedure has potential for the rapid evaluation of novel products.
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Affiliation(s)
- Milos Legner
- Micrylium Laboratories, Toronto, ON M3H 5T5, Canada;
- Correspondence:
| | - James Jonkman
- Advanced Optical Microscopy Facility, University Health Network, Toronto, ON M5G 1L7, Canada;
| | - Dean Swift
- Micrylium Laboratories, Toronto, ON M3H 5T5, Canada;
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18
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Zaki G, Gudla PR, Lee K, Kim J, Ozbun L, Shachar S, Gadkari M, Sun J, Fraser IDC, Franco LM, Misteli T, Pegoraro G. A Deep Learning Pipeline for Nucleus Segmentation. Cytometry A 2020; 97:1248-1264. [PMID: 33141508 DOI: 10.1002/cyto.a.24257] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 10/28/2020] [Accepted: 10/30/2020] [Indexed: 11/08/2022]
Abstract
Deep learning is rapidly becoming the technique of choice for automated segmentation of nuclei in biological image analysis workflows. In order to evaluate the feasibility of training nuclear segmentation models on small, custom annotated image datasets that have been augmented, we have designed a computational pipeline to systematically compare different nuclear segmentation model architectures and model training strategies. Using this approach, we demonstrate that transfer learning and tuning of training parameters, such as the composition, size, and preprocessing of the training image dataset, can lead to robust nuclear segmentation models, which match, and often exceed, the performance of existing, off-the-shelf deep learning models pretrained on large image datasets. We envision a practical scenario where deep learning nuclear segmentation models trained in this way can be shared across a laboratory, facility, or institution, and continuously improved by training them on progressively larger and varied image datasets. Our work provides computational tools and a practical framework for deep learning-based biological image segmentation using small annotated image datasets. Published [2020]. This article is a U.S. Government work and is in the public domain in the USA.
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Affiliation(s)
- George Zaki
- Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research (FNLCR), Frederick, Maryland, USA
| | - Prabhakar R Gudla
- High-Throughput Imaging Facility (HiTIF), Center for Cancer Research (CCR), NCI/NIH, Bethesda, Maryland, USA
| | - Kyunghun Lee
- High-Throughput Imaging Facility (HiTIF), Center for Cancer Research (CCR), NCI/NIH, Bethesda, Maryland, USA
| | - Justin Kim
- Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research (FNLCR), Frederick, Maryland, USA.,Pratt School of Engineering, Duke University, Durham, North Carolina, USA
| | - Laurent Ozbun
- High-Throughput Imaging Facility (HiTIF), Center for Cancer Research (CCR), NCI/NIH, Bethesda, Maryland, USA
| | - Sigal Shachar
- Cell Biology of Genomes (CBGE), Center for Cancer Research (CCR), NCI/NIH, Bethesda, Maryland, USA
| | - Manasi Gadkari
- Systemic Autoimmunity Branch, NIAMS/NIH, Bethesda, Maryland, USA
| | - Jing Sun
- Laboratory of Immune System Biology, NIAID/NIH, Bethesda, Maryland, USA
| | - Iain D C Fraser
- Laboratory of Immune System Biology, NIAID/NIH, Bethesda, Maryland, USA
| | - Luis M Franco
- Systemic Autoimmunity Branch, NIAMS/NIH, Bethesda, Maryland, USA
| | - Tom Misteli
- Cell Biology of Genomes (CBGE), Center for Cancer Research (CCR), NCI/NIH, Bethesda, Maryland, USA
| | - Gianluca Pegoraro
- High-Throughput Imaging Facility (HiTIF), Center for Cancer Research (CCR), NCI/NIH, Bethesda, Maryland, USA
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19
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Laux L, Cutiongco MFA, Gadegaard N, Jensen BS. Interactive machine learning for fast and robust cell profiling. PLoS One 2020; 15:e0237972. [PMID: 32915784 PMCID: PMC7485821 DOI: 10.1371/journal.pone.0237972] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 08/07/2020] [Indexed: 01/22/2023] Open
Abstract
Automated profiling of cell morphology is a powerful tool for inferring cell function. However, this technique retains a high barrier to entry. In particular, configuring image processing parameters for optimal cell profiling is susceptible to cognitive biases and dependent on user experience. Here, we use interactive machine learning to identify the optimum cell profiling configuration that maximises quality of the cell profiling outcome. The process is guided by the user, from whom a rating of the quality of a cell profiling configuration is obtained. We use Bayesian optimisation, an established machine learning algorithm, to learn from this information and automatically recommend the next configuration to examine with the aim of maximising the quality of the processing or analysis. Compared to existing interactive machine learning tools that require domain expertise for per-class or per-pixel annotations, we rely on users’ explicit assessment of output quality of the cell profiling task at hand. We validated our interactive approach against the standard human trial-and-error scheme to optimise an object segmentation task using the standard software CellProfiler. Our toolkit enabled rapid optimisation of an object segmentation pipeline, increasing the quality of object segmentation over a pipeline optimised through trial-and-error. Users also attested to the ease of use and reduced cognitive load enabled by our machine learning strategy over the standard approach. We envision that our interactive machine learning approach can enhance the quality and efficiency of pipeline optimisation to democratise image-based cell profiling.
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Affiliation(s)
- Lisa Laux
- School of Computing Science, University of Glasgow, Glasgow, Scotland
| | - Marie F. A. Cutiongco
- School of Engineering, Biomedical Engineering, University of Glasgow, Glasgow, Scotland
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, England
| | - Nikolaj Gadegaard
- School of Engineering, Biomedical Engineering, University of Glasgow, Glasgow, Scotland
| | - Bjørn Sand Jensen
- School of Computing Science, University of Glasgow, Glasgow, Scotland
- * E-mail:
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20
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Husna N, Gascoigne NRJ, Tey HL, Ng LG, Tan Y. Reprint of "Multi-modal image cytometry approach - From dynamic to whole organ imaging". Cell Immunol 2020; 350:104086. [PMID: 32169249 DOI: 10.1016/j.cellimm.2020.104086] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 06/18/2019] [Accepted: 06/18/2019] [Indexed: 12/13/2022]
Abstract
Optical imaging is a valuable tool to visualise biological processes in the context of the tissue. Each imaging modality provides the biologist with different types of information - cell dynamics and migration over time can be tracked with time-lapse imaging (e.g. intra-vital imaging); an overview of whole tissues can be acquired using optical clearing in conjunction with light sheet microscopy; finer details such as cellular morphology and fine nerve tortuosity can be imaged at higher resolution using the confocal microscope. Multi-modal imaging combined with image cytometry - a form of quantitative analysis of image datasets - provides an objective basis for comparing between sample groups. Here, we provide an overview of technical aspects to look out for in an image cytometry workflow, and discuss issues related to sample preparation, image post-processing and analysis for intra-vital and whole organ imaging.
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Affiliation(s)
- Nazihah Husna
- Singapore Immunology Network (SIgN), A*STAR (Agency for Science, Technology and Research), Biopolis, 8A Biomedical Grove, Singapore 138648, Singapore; Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, 5 Science Drive 2, Singapore 117545, Singapore
| | - Nicholas R J Gascoigne
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, 5 Science Drive 2, Singapore 117545, Singapore
| | - Hong Liang Tey
- National Skin Centre, 1 Mandalay Road, Singapore 308205, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, 11 Mandalay Road, Singapore 308232, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Dr, Singapore 117597, Singapore
| | - Lai Guan Ng
- Singapore Immunology Network (SIgN), A*STAR (Agency for Science, Technology and Research), Biopolis, 8A Biomedical Grove, Singapore 138648, Singapore; Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, 5 Science Drive 2, Singapore 117545, Singapore.
| | - Yingrou Tan
- Singapore Immunology Network (SIgN), A*STAR (Agency for Science, Technology and Research), Biopolis, 8A Biomedical Grove, Singapore 138648, Singapore; National Skin Centre, 1 Mandalay Road, Singapore 308205, Singapore.
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21
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Ortell KK, Switonski PM, Delaney JR. FairSubset: A tool to choose representative subsets of data for use with replicates or groups of different sample sizes. J Biol Methods 2019; 6:e118. [PMID: 31583263 PMCID: PMC6761370 DOI: 10.14440/jbm.2019.299] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 08/04/2019] [Accepted: 08/04/2019] [Indexed: 11/23/2022] Open
Abstract
High-impact journals are promoting transparency of data. Modern scientific methods can be automated and produce disparate samples sizes. In many cases, it is desirable to retain identical or pre-defined sample sizes between replicates or groups. However, choosing which subset of originally acquired data that best matches the entirety of the data set without introducing bias is not trivial. Here, we released a free online tool, FairSubset, and its constituent Shiny App R code to subset data in an unbiased fashion. Subsets were set at the same N across samples and retained representative average and standard deviation information. The method can be used for quantitation of entire fields of view or other replicates without biasing the data pool toward large N samples. We showed examples of the tool’s use with fluorescence data and DNA-damage related Comet tail quantitation. This FairSubset tool and the method to retain distribution information at the single-datum level may be considered for standardized use in fair publishing practices.
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Affiliation(s)
- Katherine K Ortell
- Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Pawel M Switonski
- Departments of Neurology, Duke University School of Medicine, Durham, NC 27710, USA.,The Duke Center for Neurodegeneration & Neurotherapeutics, Duke University School of Medicine, Durham, NC 27710, USA
| | - Joe Ryan Delaney
- Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC 29425, USA
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22
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Husna N, Gascoigne NR, Tey HL, Ng LG, Tan Y. Multi-modal image cytometry approach – From dynamic to whole organ imaging. Cell Immunol 2019; 344:103946. [DOI: 10.1016/j.cellimm.2019.103946] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 06/18/2019] [Accepted: 06/18/2019] [Indexed: 12/27/2022]
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23
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de Souza Lima IM, Schiavinato JLDS, Paulino Leite SB, Sastre D, Bezerra HLDO, Sangiorgi B, Corveloni AC, Thomé CH, Faça VM, Covas DT, Zago MA, Giacca M, Mano M, Panepucci RA. High-content screen in human pluripotent cells identifies miRNA-regulated pathways controlling pluripotency and differentiation. Stem Cell Res Ther 2019; 10:202. [PMID: 31287022 PMCID: PMC6615276 DOI: 10.1186/s13287-019-1318-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 06/11/2019] [Accepted: 06/30/2019] [Indexed: 01/13/2023] Open
Abstract
Background By post-transcriptionally regulating multiple target transcripts, microRNAs (miRNAs or miR) play important biological functions. H1 embryonic stem cells (hESCs) and NTera-2 embryonal carcinoma cells (ECCs) are two of the most widely used human pluripotent model cell lines, sharing several characteristics, including the expression of miRNAs associated to the pluripotent state or with differentiation. However, how each of these miRNAs functionally impacts the biological properties of these cells has not been systematically evaluated. Methods We investigated the effects of 31 miRNAs on NTera-2 and H1 hESCs, by transfecting miRNA mimics. Following 3–4 days of culture, cells were stained for the pluripotency marker OCT4 and the G2 cell-cycle marker Cyclin B1, and nuclei and cytoplasm were co-stained with Hoechst and Cell Mask Blue, respectively. By using automated quantitative fluorescence microscopy (i.e., high-content screening (HCS)), we obtained several morphological and marker intensity measurements, in both cell compartments, allowing the generation of a multiparametric miR-induced phenotypic profile describing changes related to proliferation, cell cycle, pluripotency, and differentiation. Results Despite the overall similarities between both cell types, some miRNAs elicited cell-specific effects, while some related miRNAs induced contrasting effects in the same cell. By identifying transcripts predicted to be commonly targeted by miRNAs inducing similar effects (profiles grouped by hierarchical clustering), we were able to uncover potentially modulated signaling pathways and biological processes, likely mediating the effects of the microRNAs on the distinct groups identified. Specifically, we show that miR-363 contributes to pluripotency maintenance, at least in part, by targeting NOTCH1 and PSEN1 and inhibiting Notch-induced differentiation, a mechanism that could be implicated in naïve and primed pluripotent states. Conclusions We present the first multiparametric high-content microRNA functional screening in human pluripotent cells. Integration of this type of data with similar data obtained from siRNA screenings (using the same HCS assay) could provide a large-scale functional approach to identify and validate microRNA-mediated regulatory mechanisms controlling pluripotency and differentiation. Electronic supplementary material The online version of this article (10.1186/s13287-019-1318-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ildercílio Mota de Souza Lima
- Laboratory of Functional Biology (LFBio), Center for Cell-Based Therapy (CTC), Regional Blood Center of Ribeirão Preto, Rua Tenente Catão Roxo, 2501, Ribeirão Preto, SP, CEP: 14051-140, Brazil.,Department of Genetics and Internal Medicine, Ribeirao Preto Medical School, University of São Paulo (FMRP-USP), Ribeirão Preto, SP, Brazil
| | - Josiane Lilian Dos Santos Schiavinato
- Laboratory of Functional Biology (LFBio), Center for Cell-Based Therapy (CTC), Regional Blood Center of Ribeirão Preto, Rua Tenente Catão Roxo, 2501, Ribeirão Preto, SP, CEP: 14051-140, Brazil.,Department of Genetics and Internal Medicine, Ribeirao Preto Medical School, University of São Paulo (FMRP-USP), Ribeirão Preto, SP, Brazil
| | - Sarah Blima Paulino Leite
- Laboratory of Functional Biology (LFBio), Center for Cell-Based Therapy (CTC), Regional Blood Center of Ribeirão Preto, Rua Tenente Catão Roxo, 2501, Ribeirão Preto, SP, CEP: 14051-140, Brazil.,Department of Genetics and Internal Medicine, Ribeirao Preto Medical School, University of São Paulo (FMRP-USP), Ribeirão Preto, SP, Brazil
| | - Danuta Sastre
- Laboratory of Functional Biology (LFBio), Center for Cell-Based Therapy (CTC), Regional Blood Center of Ribeirão Preto, Rua Tenente Catão Roxo, 2501, Ribeirão Preto, SP, CEP: 14051-140, Brazil
| | - Hudson Lenormando de Oliveira Bezerra
- Laboratory of Functional Biology (LFBio), Center for Cell-Based Therapy (CTC), Regional Blood Center of Ribeirão Preto, Rua Tenente Catão Roxo, 2501, Ribeirão Preto, SP, CEP: 14051-140, Brazil.,Department of Genetics and Internal Medicine, Ribeirao Preto Medical School, University of São Paulo (FMRP-USP), Ribeirão Preto, SP, Brazil
| | - Bruno Sangiorgi
- Laboratory of Functional Biology (LFBio), Center for Cell-Based Therapy (CTC), Regional Blood Center of Ribeirão Preto, Rua Tenente Catão Roxo, 2501, Ribeirão Preto, SP, CEP: 14051-140, Brazil.,Department of Genetics and Internal Medicine, Ribeirao Preto Medical School, University of São Paulo (FMRP-USP), Ribeirão Preto, SP, Brazil
| | - Amanda Cristina Corveloni
- Laboratory of Functional Biology (LFBio), Center for Cell-Based Therapy (CTC), Regional Blood Center of Ribeirão Preto, Rua Tenente Catão Roxo, 2501, Ribeirão Preto, SP, CEP: 14051-140, Brazil.,Department of Genetics and Internal Medicine, Ribeirao Preto Medical School, University of São Paulo (FMRP-USP), Ribeirão Preto, SP, Brazil
| | - Carolina Hassibe Thomé
- Department of Biochemistry and Immunology, Ribeirão Preto Medical School, University of São Paulo (FMRP-USP), Ribeirão Preto, Brazil
| | - Vitor Marcel Faça
- Department of Biochemistry and Immunology, Ribeirão Preto Medical School, University of São Paulo (FMRP-USP), Ribeirão Preto, Brazil
| | - Dimas Tadeu Covas
- Laboratory of Functional Biology (LFBio), Center for Cell-Based Therapy (CTC), Regional Blood Center of Ribeirão Preto, Rua Tenente Catão Roxo, 2501, Ribeirão Preto, SP, CEP: 14051-140, Brazil.,Department of Genetics and Internal Medicine, Ribeirao Preto Medical School, University of São Paulo (FMRP-USP), Ribeirão Preto, SP, Brazil
| | - Marco Antônio Zago
- Laboratory of Functional Biology (LFBio), Center for Cell-Based Therapy (CTC), Regional Blood Center of Ribeirão Preto, Rua Tenente Catão Roxo, 2501, Ribeirão Preto, SP, CEP: 14051-140, Brazil.,Department of Genetics and Internal Medicine, Ribeirao Preto Medical School, University of São Paulo (FMRP-USP), Ribeirão Preto, SP, Brazil
| | - Mauro Giacca
- Molecular Medicine Laboratory, International Centre for Genetic and Engineering and Biotechnology (ICGEB), Trieste, Italy
| | - Miguel Mano
- Molecular Medicine Laboratory, International Centre for Genetic and Engineering and Biotechnology (ICGEB), Trieste, Italy.,Center for Neuroscience and Cell Biology (CNC), University of Coimbra, Coimbra, Portugal
| | - Rodrigo Alexandre Panepucci
- Laboratory of Functional Biology (LFBio), Center for Cell-Based Therapy (CTC), Regional Blood Center of Ribeirão Preto, Rua Tenente Catão Roxo, 2501, Ribeirão Preto, SP, CEP: 14051-140, Brazil. .,Department of Genetics and Internal Medicine, Ribeirao Preto Medical School, University of São Paulo (FMRP-USP), Ribeirão Preto, SP, Brazil.
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24
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St-Georges-Robillard A, Cahuzac M, Péant B, Fleury H, Lateef MA, Ricard A, Sauriol A, Leblond F, Mes-Masson AM, Gervais T. Long-term fluorescence hyperspectral imaging of on-chip treated co-culture tumour spheroids to follow clonal evolution. Integr Biol (Camb) 2019; 11:130-141. [PMID: 31172192 DOI: 10.1093/intbio/zyz012] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 03/09/2019] [Accepted: 05/30/2019] [Indexed: 12/22/2022]
Abstract
Multicellular tumour spheroids are an ideal in vitro tumour model to study clonal heterogeneity and drug resistance in cancer research because different cell types can be mixed at will. However, measuring the individual response of each cell population over time is challenging: current methods are either destructive, such as flow cytometry, or cannot image throughout a spheroid, such as confocal microscopy. Our group previously developed a wide-field fluorescence hyperspectral imaging system to study spheroids formed and cultured in microfluidic chips. In the present study, two subclones of a single parental ovarian cancer cell line transfected to express different fluorophores were produced and co-culture spheroids were formed on-chip using ratios forming highly asymmetric subpopulations. We performed a 3D proliferation assay on each cell population forming the spheroids that matched the 2D growth behaviour. Response assays to PARP inhibitors and platinum-based drugs were also performed to follow the clonal evolution of mixed populations. Our experiments show that hyperspectral imaging can detect spheroid response before observing a decrease in spheroid diameter. Hyperspectral imaging and microfluidic-based spheroid assays provide a versatile solution to study clonal heterogeneity, able to measure response in subpopulations presenting as little as 10% of the initial spheroid.
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Affiliation(s)
- Amélie St-Georges-Robillard
- Polytechnique Montréal, Department of Engineering Physics and Institute of Biomedical Engineering, Montreal, Canada
- Centre de recherche du Centre hospitalier de l'Université de Montréal and Institut du cancer de Montréal, Montreal, Canada
| | - Maxime Cahuzac
- Centre de recherche du Centre hospitalier de l'Université de Montréal and Institut du cancer de Montréal, Montreal, Canada
| | - Benjamin Péant
- Centre de recherche du Centre hospitalier de l'Université de Montréal and Institut du cancer de Montréal, Montreal, Canada
- TransMedTech Institute, Montréal, Canada
| | - Hubert Fleury
- Centre de recherche du Centre hospitalier de l'Université de Montréal and Institut du cancer de Montréal, Montreal, Canada
| | - Muhammad Abdul Lateef
- Centre de recherche du Centre hospitalier de l'Université de Montréal and Institut du cancer de Montréal, Montreal, Canada
| | - Alexis Ricard
- Centre de recherche du Centre hospitalier de l'Université de Montréal and Institut du cancer de Montréal, Montreal, Canada
| | - Alexandre Sauriol
- Centre de recherche du Centre hospitalier de l'Université de Montréal and Institut du cancer de Montréal, Montreal, Canada
| | - Frédéric Leblond
- Polytechnique Montréal, Department of Engineering Physics and Institute of Biomedical Engineering, Montreal, Canada
- Centre de recherche du Centre hospitalier de l'Université de Montréal and Institut du cancer de Montréal, Montreal, Canada
| | - Anne-Marie Mes-Masson
- Centre de recherche du Centre hospitalier de l'Université de Montréal and Institut du cancer de Montréal, Montreal, Canada
- Université de Montréal, Department of Medicine, Montreal, Canada
| | - Thomas Gervais
- Polytechnique Montréal, Department of Engineering Physics and Institute of Biomedical Engineering, Montreal, Canada
- Centre de recherche du Centre hospitalier de l'Université de Montréal and Institut du cancer de Montréal, Montreal, Canada
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25
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Svenningsen EB, Poulsen TB. Establishing cell painting in a smaller chemical biology lab - A report from the frontier. Bioorg Med Chem 2019; 27:2609-2615. [PMID: 30935791 DOI: 10.1016/j.bmc.2019.03.052] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 03/26/2019] [Indexed: 12/18/2022]
Abstract
In this paper we will outline the efforts we have made recently to establish the profiling platform known as cell painting in our laboratory. This platform, which is based on fluorescence microscopy, allows rapid and cheap access to bioactivity fingerprints for small molecules and thereby can contribute with important information in many experimental situations that is faced in laboratories involved in molecular probe design, mode-of-action studies or that perform focused phenotypic screens. We have tried to achieve the following two objectives: (1) provide a detailed description of the hurdles that we had to overcome during establishment and describe our final protocol; (2) provide a more pedagogical description of the different methods used to analyse and represent data from this experiment. Finally, we provide an example of how the method can be used to clarify mechanistic dichotomies.
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Affiliation(s)
- Esben B Svenningsen
- Department of Chemistry - Aarhus University, Langelandsgade 140, DK-8000, Denmark
| | - Thomas B Poulsen
- Department of Chemistry - Aarhus University, Langelandsgade 140, DK-8000, Denmark.
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26
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Singh R, Beasley R, Long T, Caffrey CR. Algorithmic Mapping and Characterization of the Drug-Induced Phenotypic-Response Space of Parasites Causing Schistosomiasis. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:469-481. [PMID: 27071187 PMCID: PMC5915339 DOI: 10.1109/tcbb.2016.2550444] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Neglected tropical diseases, especially those caused by helminths, constitute some of the most common infections of the world's poorest people. Amongst these, schistosomiasis (bilharzia or 'snail fever'), caused by blood flukes of the genus Schistosoma, ranks second only to malaria in terms of human impact: two hundred million people are infected and close to 800 million are at risk of infection. Drug screening against helminths poses unique challenges: the parasite cannot be cloned and is difficult to target using gene knockouts or RNAi. Consequently, both lead identification and validation involve phenotypic screening, where parasites are exposed to compounds whose effects are determined through the analysis of the ensuing phenotypic responses. The efficacy of leads thus identified derives from one or more or even unknown molecular mechanisms of action. The two most immediate and significant challenges that confront the state-of-the-art in this area are: the development of automated and quantitative phenotypic screening techniques and the mapping and quantitative characterization of the totality of phenotypic responses of the parasite. In this paper, we investigate and propose solutions for the latter problem in terms of the following: (1) mathematical formulation and algorithms that allow rigorous representation of the phenotypic response space of the parasite, (2) application of graph-theoretic and network analysis techniques for quantitative modeling and characterization of the phenotypic space, and (3) application of the aforementioned methodology to analyze the phenotypic space of S. mansoni - one of the etiological agents of schistosomiasis, induced by compounds that target its polo-like kinase 1 (PLK 1) gene - a recently validated drug target. In our approach, first, bio-image analysis algorithms are used to quantify the phenotypic responses of different drugs. Next, these responses are linearly mapped into a low- dimensional space using Principle Component Analysis (PCA). The phenotype space is modeled using neighborhood graphs which are used to represent the similarity amongst the phenotypes. These graphs are characterized and explored using network analysis algorithms. We present a number of results related to both the nature of the phenotypic space of the S. mansoni parasite as well as algorithmic issues encountered in constructing and analyzing the phenotypic-response space. In particular, the phenotype distribution of the parasite was found to have a distinct shape and topology. We have also quantitatively characterized the phenotypic space by varying critical model parameters. Finally, these maps of the phenotype space allows visualization and reasoning about complex relationships between putative drugs and their system-wide effects and can serve as a highly efficient paradigm for assimilating and unifying information from phenotypic screens both during lead identification and lead optimization.
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Harrill JA. Human-Derived Neurons and Neural Progenitor Cells in High Content Imaging Applications. Methods Mol Biol 2018; 1683:305-338. [PMID: 29082500 DOI: 10.1007/978-1-4939-7357-6_18] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Due to advances in the fields of stem cell biology and cellular engineering, a variety of commercially available human-derived neurons and neural progenitor cells (NPCs) are now available for use in research applications, including small molecule efficacy or toxicity screening. The use of human-derived neural cells is anticipated to address some of the uncertainties associated with the use of nonhuman culture models or transformed cell lines derived from human tissues. Many of the human-derived neurons and NPCs currently available from commercial sources recapitulate critical process of nervous system development including NPC proliferation, neurite outgrowth, synaptogenesis, and calcium signaling, each of which can be evaluated using high content image analysis (HCA). Human-derived neurons and NPCs are also amenable to culture in multiwell plate formats and thus may be adapted for use in HCA-based screening applications. This article reviews various types of HCA-based assays that have been used in conjunction with human-derived neurons and NPC cultures. This article also highlights instances where lower throughput analysis of neurodevelopmental processes has been performed and which demonstrate a potential for adaptation to higher-throughout imaging methods. Finally, a generic protocol for evaluating neurite outgrowth in human-derived neurons using a combination of immunocytochemistry and HCA is presented. The information provided in this article is intended to serve as a resource for cell model and assay selection for those interested in evaluating neurodevelopmental processes in human-derived cells.
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Affiliation(s)
- Joshua A Harrill
- Center for Toxicology and Environmental Health, LLC, 5120 Northshore Drive, Little Rock, AR, 72118, USA.
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28
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Core JQ, Mehrabi M, Robinson ZR, Ochs AR, McCarthy LA, Zaragoza MV, Grosberg A. Age of heart disease presentation and dysmorphic nuclei in patients with LMNA mutations. PLoS One 2017; 12:e0188256. [PMID: 29149195 PMCID: PMC5693421 DOI: 10.1371/journal.pone.0188256] [Citation(s) in RCA: 9] [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: 08/17/2017] [Accepted: 11/05/2017] [Indexed: 01/24/2023] Open
Abstract
Nuclear shape defects are a distinguishing characteristic in laminopathies, cancers, and other pathologies. Correlating these defects to the symptoms, mechanisms, and progression of disease requires unbiased, quantitative, and high-throughput means of quantifying nuclear morphology. To accomplish this, we developed a method of automatically segmenting fluorescently stained nuclei in 2D microscopy images and then classifying them as normal or dysmorphic based on three geometric features of the nucleus using a package of Matlab codes. As a test case, cultured skin-fibroblast nuclei of individuals possessing LMNA splice-site mutation (c.357-2A>G), LMNA nonsense mutation (c.736 C>T, pQ246X) in exon 4, LMNA missense mutation (c.1003C>T, pR335W) in exon 6, Hutchinson-Gilford Progeria Syndrome, and no LMNA mutations were analyzed. For each cell type, the percentage of dysmorphic nuclei, and other morphological features such as average nuclear area and average eccentricity were obtained. Compared to blind observers, our procedure implemented in Matlab codes possessed similar accuracy to manual counting of dysmorphic nuclei while being significantly more consistent. The automatic quantification of nuclear defects revealed a correlation between in vitro results and age of patients for initial symptom onset. Our results demonstrate the method’s utility in experimental studies of diseases affecting nuclear shape through automated, unbiased, and accurate identification of dysmorphic nuclei.
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Affiliation(s)
- Jason Q. Core
- Departments of Biomedical Engineering, University of California, Irvine, CA, United States of America
- The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, Irvine, CA, United States of America
| | - Mehrsa Mehrabi
- Departments of Biomedical Engineering, University of California, Irvine, CA, United States of America
- The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, Irvine, CA, United States of America
| | - Zachery R. Robinson
- Departments of Biomedical Engineering, University of California, Irvine, CA, United States of America
- The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, Irvine, CA, United States of America
| | - Alexander R. Ochs
- Departments of Biomedical Engineering, University of California, Irvine, CA, United States of America
- The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, Irvine, CA, United States of America
| | - Linda A. McCarthy
- Departments of Biomedical Engineering, University of California, Irvine, CA, United States of America
- The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, Irvine, CA, United States of America
| | - Michael V. Zaragoza
- Pediatrics–Genetics & Genomics Division–School of Medicine, University of California, Irvine, CA, United States of America
- Biological Chemistry–School of Medicine, University of California, Irvine, CA, United States of America
| | - Anna Grosberg
- Departments of Biomedical Engineering, University of California, Irvine, CA, United States of America
- The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, Irvine, CA, United States of America
- Chemical Engineering and Materials Science, University of California, Irvine, CA, United States of America
- * E-mail:
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Wang M, Ong LLS, Dauwels J, Asada HH. Automated tracking and quantification of angiogenic vessel formation in 3D microfluidic devices. PLoS One 2017; 12:e0186465. [PMID: 29136008 PMCID: PMC5685595 DOI: 10.1371/journal.pone.0186465] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Accepted: 10/02/2017] [Indexed: 11/19/2022] Open
Abstract
Angiogenesis, the growth of new blood vessels from pre-existing vessels, is a critical step in cancer invasion. Better understanding of the angiogenic mechanisms is required to develop effective antiangiogenic therapies for cancer treatment. We culture angiogenic vessels in 3D microfluidic devices under different Sphingosin-1-phosphate (S1P) conditions and develop an automated vessel formation tracking system (AVFTS) to track the angiogenic vessel formation and extract quantitative vessel information from the experimental time-lapse phase contrast images. The proposed AVFTS first preprocesses the experimental images, then applies a distance transform and an augmented fast marching method in skeletonization, and finally implements the Hungarian method in branch tracking. When applying the AVFTS to our experimental data, we achieve 97.3% precision and 93.9% recall by comparing with the ground truth obtained from manual tracking by visual inspection. This system enables biologists to quantitatively compare the influence of different growth factors. Specifically, we conclude that the positive S1P gradient increases cell migration and vessel elongation, leading to a higher probability for branching to occur. The AVFTS is also applicable to distinguish tip and stalk cells by considering the relative cell locations in a branch. Moreover, we generate a novel type of cell lineage plot, which not only provides cell migration and proliferation histories but also demonstrates cell phenotypic changes and branch information.
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Affiliation(s)
- Mengmeng Wang
- School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore, Singapore
- Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
| | | | - Justin Dauwels
- School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore, Singapore
| | - H. Harry Asada
- Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
- Department of Mechanical Engineering, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
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30
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Ahonen I, Åkerfelt M, Toriseva M, Oswald E, Schüler J, Nees M. A high-content image analysis approach for quantitative measurements of chemosensitivity in patient-derived tumor microtissues. Sci Rep 2017; 7:6600. [PMID: 28747710 PMCID: PMC5529420 DOI: 10.1038/s41598-017-06544-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Accepted: 06/14/2017] [Indexed: 11/17/2022] Open
Abstract
Organotypic, three-dimensional (3D) cancer models have enabled investigations of complex microtissues in increasingly realistic conditions. However, a drawback of these advanced models remains the poor biological relevance of cancer cell lines, while higher clinical significance would be obtainable with patient-derived cell cultures. Here, we describe the generation and data analysis of 3D microtissue models from patient-derived xenografts (PDX) of non-small cell lung carcinoma (NSCLC). Standard of care anti-cancer drugs were applied and the altered multicellular morphologies were captured by confocal microscopy, followed by automated image analyses to quantitatively measure phenotypic features for high-content chemosensitivity tests. The obtained image data were thresholded using a local entropy filter after which the image foreground was split into local regions, for a supervised classification into tumor or fibroblast cell types. Robust statistical methods were applied to evaluate treatment effects on growth and morphology. Both novel and existing computational approaches were compared at each step, while prioritizing high experimental throughput. Docetaxel was found to be the most effective drug that blocked both tumor growth and invasion. These effects were also validated in PDX tumors in vivo. Our research opens new avenues for high-content drug screening based on patient-derived cell cultures, and for personalized chemosensitivity testing.
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Affiliation(s)
- Ilmari Ahonen
- Department of Mathematics and Statistics, University of Turku, Turku, Finland. .,Institute of Biomedicine, University of Turku, Turku, Finland.
| | - Malin Åkerfelt
- Institute of Biomedicine, University of Turku, Turku, Finland
| | - Mervi Toriseva
- Institute of Biomedicine, University of Turku, Turku, Finland
| | - Eva Oswald
- Discovery Services, Charles River, Freiburg, Germany
| | - Julia Schüler
- Discovery Services, Charles River, Freiburg, Germany
| | - Matthias Nees
- Institute of Biomedicine, University of Turku, Turku, Finland
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31
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Pegoraro G, Misteli T. High-Throughput Imaging for the Discovery of Cellular Mechanisms of Disease. Trends Genet 2017; 33:604-615. [PMID: 28732598 DOI: 10.1016/j.tig.2017.06.005] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Revised: 06/19/2017] [Accepted: 06/20/2017] [Indexed: 12/23/2022]
Abstract
High-throughput imaging (HTI) is a powerful tool in the discovery of cellular disease mechanisms. While traditional approaches to identify disease pathways often rely on knowledge of the causative genetic defect, HTI-based screens offer an unbiased discovery approach based on any morphological or functional defects of disease cells or tissues. In this review, we provide an overview of the use of HTI for the study of human disease mechanisms. We discuss key technical aspects of HTI and highlight representative examples of its practical applications for the discovery of molecular mechanisms of disease, focusing on infectious diseases and host-pathogen interactions, cancer, and rare genetic diseases. We also present some of the current challenges and possible solutions offered by novel cell culture systems and genome engineering approaches.
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Affiliation(s)
- Gianluca Pegoraro
- NCI High-Throughput Imaging Facility, Bethesda, MD 20892, USA; Center for Cancer Research, National Cancer Institute/NIH, Bethesda, MD 20892, USA.
| | - Tom Misteli
- Center for Cancer Research, National Cancer Institute/NIH, Bethesda, MD 20892, USA.
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32
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Abstract
Automated analysis of microscope images is necessitated by the increased need for high-resolution follow up of events in time. Manually finding the right images to be analyzed, or eliminated from data analysis are common day-to-day problems in microscopy research today, and the constantly growing size of image datasets does not help the matter. We propose a simple method and a software tool for sorting images within a dataset, according to their relative quality. We demonstrate the applicability of our method in finding good quality images in a STED microscope sample preparation optimization image dataset. The results are validated by comparisons to subjective opinion scores, as well as five state-of-the-art blind image quality assessment methods. We also show how our method can be applied to eliminate useless out-of-focus images in a High-Content-Screening experiment. We further evaluate the ability of our image quality ranking method to detect out-of-focus images, by extensive simulations, and by comparing its performance against previously published, well-established microscopy autofocus metrics.
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33
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Ahonen I, Härmä V, Schukov HP, Nees M, Nevalainen J. Morphological Clustering of Cell Cultures Based on Size, Shape, and Texture Features. Stat Biopharm Res 2016. [DOI: 10.1080/19466315.2016.1146162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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34
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Noller CM, Boulina M, McNamara G, Szeto A, McCabe PM, Mendez AJ. A Practical Approach to Quantitative Processing and Analysis of Small Biological Structures by Fluorescent Imaging. J Biomol Tech 2016; 27:90-7. [PMID: 27182204 DOI: 10.7171/jbt.16-2703-001] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Standards in quantitative fluorescent imaging are vaguely recognized and receive insufficient discussion. A common best practice is to acquire images at Nyquist rate, where highest signal frequency is assumed to be the highest obtainable resolution of the imaging system. However, this particular standard is set to insure that all obtainable information is being collected. The objective of the current study was to demonstrate that for quantification purposes, these correctly set acquisition rates can be redundant; instead, linear size of the objects of interest can be used to calculate sufficient information density in the image. We describe optimized image acquisition parameters and unbiased methods for processing and quantification of medium-size cellular structures. Sections of rabbit aortas were immunohistochemically stained to identify and quantify sympathetic varicosities, >2 μm in diameter. Images were processed to reduce background noise and segment objects using free, open-access software. Calculations of the optimal sampling rate for the experiment were based on the size of the objects of interest. The effect of differing sampling rates and processing techniques on object quantification was demonstrated. Oversampling led to a substantial increase in file size, whereas undersampling hindered reliable quantification. Quantification of raw and incorrectly processed images generated false structures, misrepresenting the underlying data. The current study emphasizes the importance of defining image-acquisition parameters based on the structure(s) of interest. The proposed postacquisition processing steps effectively removed background and noise, allowed for reliable quantification, and eliminated user bias. This customizable, reliable method for background subtraction and structure quantification provides a reproducible tool for researchers across biologic disciplines.
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Affiliation(s)
- Crystal M Noller
- Department of Psychology, University of Miami, Coral Gables, Florida 33124, USA
| | - Maria Boulina
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, Florida 33136, USA
| | - George McNamara
- Department of Pediatrics-Research, University of Texas MD Anderson Cancer Center, Houston, Texas 77450, USA
| | - Angela Szeto
- Department of Psychology, University of Miami, Coral Gables, Florida 33124, USA
| | - Philip M McCabe
- Department of Psychology, University of Miami, Coral Gables, Florida 33124, USA
| | - Armando J Mendez
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, Florida 33136, USA; ; Department of Medicine, University of Miami Miller School of Medicine, Miami, Florida 33136, USA; and
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35
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Talapatra SN, Mitra P, Swarnakar S. Morphology and Phenotype of Peripheral Erythrocytes of Fish: A Rapid Screening of Images by Using Software. INTERNATIONAL LETTERS OF NATURAL SCIENCES 2016. [DOI: 10.18052/www.scipress.com/ilns.54.27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Many information of biological study as stained cells analysis under microscope cannot be obtained rich information like detail morphology, shape, size, proper intensity etc. but image analysis software can easily be detected all these parameters within short duration. The cells types can be yeast cells to mammalian cells. An attempt has been made to detect cellular abnormalities from an image of metronidazole (MTZ) treated compared to control images of peripheral erythrocytes of fish by using non-commercial, open-source, CellProfiler (CP) image analysis software (Ver. 2.1.0). The comparative results were obtained after analysis the software. In conclusion, this image based screening of Giemsa stained fish erythrocytes can be a suitable tool in biological research for primary toxicity prediction at DNA level alongwith cellular phenotypes. Moreover, still suggestions are needed in relation to accuracy of present analysis for Giemsa stained fish erythrocytes because previous works have been carried out images of cells with fluorescence dye.
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36
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Leavesley SJ, Nakhmani A, Gao Y, Rich TC. Automated image analysis of FRET signals for subcellular cAMP quantification. Methods Mol Biol 2015; 1294:59-70. [PMID: 25783877 DOI: 10.1007/978-1-4939-2537-7_5] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
A variety of FRET probes have been developed to examine cAMP localization and dynamics in single cells. These probes offer a readily accessible approach to measure localized cAMP signals. However, given the low signal-to-noise ratio of most FRET probes and the dynamic nature of the intracellular environment, there have been marked limitations in the ability to use FRET probes to study localized signaling events within the same cell. Here, we outline a methodology to dissect kinetics of cAMP-mediated FRET signals in single cells using automated image analysis approaches. We additionally extend these approaches to the analysis of subcellular regions. These approaches offer an unique opportunity to assess localized cAMP kinetics in an unbiased, quantitative fashion.
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Affiliation(s)
- Silas J Leavesley
- Department of Chemical and Biomolecular Engineering, University of South Alabama, 150 Jaguar Drive, SH 4129, Mobile, AL, 36688, USA,
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Courtney J, Woods E, Scholz D, Hall WW, Gautier VW. MATtrack: A MATLAB-Based Quantitative Image Analysis Platform for Investigating Real-Time Photo-Converted Fluorescent Signals in Live Cells. PLoS One 2015; 10:e0140209. [PMID: 26485569 PMCID: PMC4616565 DOI: 10.1371/journal.pone.0140209] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2015] [Accepted: 09/23/2015] [Indexed: 11/18/2022] Open
Abstract
We introduce here MATtrack, an open source MATLAB-based computational platform developed to process multi-Tiff files produced by a photo-conversion time lapse protocol for live cell fluorescent microscopy. MATtrack automatically performs a series of steps required for image processing, including extraction and import of numerical values from Multi-Tiff files, red/green image classification using gating parameters, noise filtering, background extraction, contrast stretching and temporal smoothing. MATtrack also integrates a series of algorithms for quantitative image analysis enabling the construction of mean and standard deviation images, clustering and classification of subcellular regions and injection point approximation. In addition, MATtrack features a simple user interface, which enables monitoring of Fluorescent Signal Intensity in multiple Regions of Interest, over time. The latter encapsulates a region growing method to automatically delineate the contours of Regions of Interest selected by the user, and performs background and regional Average Fluorescence Tracking, and automatic plotting. Finally, MATtrack computes convenient visualization and exploration tools including a migration map, which provides an overview of the protein intracellular trajectories and accumulation areas. In conclusion, MATtrack is an open source MATLAB-based software package tailored to facilitate the analysis and visualization of large data files derived from real-time live cell fluorescent microscopy using photoconvertible proteins. It is flexible, user friendly, compatible with Windows, Mac, and Linux, and a wide range of data acquisition software. MATtrack is freely available for download at eleceng.dit.ie/courtney/MATtrack.zip.
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Affiliation(s)
- Jane Courtney
- Dublin Institute of Technology, Kevin St, Dublin, Ireland
- * E-mail:
| | - Elena Woods
- UCD Centre for Research in Infectious Diseases, School of Medicine and Medical Science, University College Dublin (UCD), Dublin, Ireland
| | - Dimitri Scholz
- UCD Conway Institute of Biomolecular & Biomedical Research, School of Medicine and Biomedical Science University College Dublin (UCD), Dublin, Ireland
| | - William W. Hall
- UCD Centre for Research in Infectious Diseases, School of Medicine and Medical Science, University College Dublin (UCD), Dublin, Ireland
| | - Virginie W. Gautier
- UCD Centre for Research in Infectious Diseases, School of Medicine and Medical Science, University College Dublin (UCD), Dublin, Ireland
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Welling M, Ponti A, Pantazis P. Symmetry breaking in the early mammalian embryo: the case for quantitative single-cell imaging analysis. Mol Hum Reprod 2015; 22:172-81. [DOI: 10.1093/molehr/gav048] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Accepted: 08/25/2015] [Indexed: 12/23/2022] Open
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39
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CIDRE: an illumination-correction method for optical microscopy. Nat Methods 2015; 12:404-6. [DOI: 10.1038/nmeth.3323] [Citation(s) in RCA: 104] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2014] [Accepted: 12/16/2014] [Indexed: 11/08/2022]
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40
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New tools for comparing microscopy images: quantitative analysis of cell types in Bacillus subtilis. J Bacteriol 2014; 197:699-709. [PMID: 25448819 DOI: 10.1128/jb.02501-14] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Fluorescence microscopy is a method commonly used to examine individual differences between bacterial cells, yet many studies still lack a quantitative analysis of fluorescence microscopy data. Here we introduce some simple tools that microbiologists can use to analyze and compare their microscopy images. We show how image data can be converted to distribution data. These data can be subjected to a cluster analysis that makes it possible to objectively compare microscopy images. The distribution data can further be analyzed using distribution fitting. We illustrate our methods by scrutinizing two independently acquired data sets, each containing microscopy images of a doubly labeled Bacillus subtilis strain. For the first data set, we examined the expression of srfA and tapA, two genes which are expressed in surfactin-producing and matrix-producing cells, respectively. For the second data set, we examined the expression of eps and tapA; these genes are expressed in matrix-producing cells. We show that srfA is expressed by all cells in the population, a finding which contrasts with a previously reported bimodal distribution of srfA expression. In addition, we show that eps and tapA do not always have the same expression profiles, despite being expressed in the same cell type: both operons are expressed in cell chains, while single cells mainly express eps. These findings exemplify that the quantification and comparison of microscopy data can yield insights that otherwise would go unnoticed.
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41
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Sheik-Khalil E, Bray MA, Özkaya Şahin G, Scarlatti G, Jansson M, Carpenter AE, Fenyö EM. Automated image-based assay for evaluation of HIV neutralization and cell-to-cell fusion inhibition. BMC Infect Dis 2014; 14:472. [PMID: 25176034 PMCID: PMC4261578 DOI: 10.1186/1471-2334-14-472] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2014] [Accepted: 08/18/2014] [Indexed: 12/04/2022] Open
Abstract
Background Standardized techniques to detect HIV-neutralizing antibody responses are of great importance in the search for an HIV vaccine. Methods Here, we present a high-throughput, high-content automated plaque reduction (APR) assay based on automated microscopy and image analysis that allows evaluation of neutralization and inhibition of cell-cell fusion within the same assay. Neutralization of virus particles is measured as a reduction in the number of fluorescent plaques, and inhibition of cell-cell fusion as a reduction in plaque area. Results We found neutralization strength to be a significant factor in the ability of virus to form syncytia. Further, we introduce the inhibitory concentration of plaque area reduction (ICpar) as an additional measure of antiviral activity, i.e. fusion inhibition. Conclusions We present an automated image based high-throughput, high-content HIV plaque reduction assay. This allows, for the first time, simultaneous evaluation of neutralization and inhibition of cell-cell fusion within the same assay, by quantifying the reduction in number of plaques and mean plaque area, respectively. Inhibition of cell-to-cell fusion requires higher quantities of inhibitory reagent than inhibition of virus neutralization. Electronic supplementary material The online version of this article (doi:10.1186/1471-2334-14-472) contains supplementary material, which is available to authorized users.
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He Y, Meng Y, Gong H, Chen S, Zhang B, Ding W, Luo Q, Li A. An automated three-dimensional detection and segmentation method for touching cells by integrating concave points clustering and random walker algorithm. PLoS One 2014; 9:e104437. [PMID: 25111442 PMCID: PMC4128780 DOI: 10.1371/journal.pone.0104437] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2013] [Accepted: 07/14/2014] [Indexed: 11/25/2022] Open
Abstract
Characterizing cytoarchitecture is crucial for understanding brain functions and neural diseases. In neuroanatomy, it is an important task to accurately extract cell populations' centroids and contours. Recent advances have permitted imaging at single cell resolution for an entire mouse brain using the Nissl staining method. However, it is difficult to precisely segment numerous cells, especially those cells touching each other. As presented herein, we have developed an automated three-dimensional detection and segmentation method applied to the Nissl staining data, with the following two key steps: 1) concave points clustering to determine the seed points of touching cells; and 2) random walker segmentation to obtain cell contours. Also, we have evaluated the performance of our proposed method with several mouse brain datasets, which were captured with the micro-optical sectioning tomography imaging system, and the datasets include closely touching cells. Comparing with traditional detection and segmentation methods, our approach shows promising detection accuracy and high robustness.
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Affiliation(s)
- Yong He
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yunlong Meng
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shangbin Chen
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Bin Zhang
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wenxiang Ding
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
- * E-mail:
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Norrie JL, Lewandowski JP, Bouldin CM, Amarnath S, Li Q, Vokes MS, Ehrlich LIR, Harfe BD, Vokes SA. Dynamics of BMP signaling in limb bud mesenchyme and polydactyly. Dev Biol 2014; 393:270-281. [PMID: 25034710 DOI: 10.1016/j.ydbio.2014.07.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2014] [Revised: 07/03/2014] [Accepted: 07/05/2014] [Indexed: 01/20/2023]
Abstract
Mutations in the Bone Morphogenetic Protein (BMP) pathway are associated with a range of defects in skeletal formation. Genetic analysis of BMP signaling requirements is complicated by the presence of three partially redundant BMPs that are required for multiple stages of limb development. We generated an inducible allele of a BMP inhibitor, Gremlin, which reduces BMP signaling. We show that BMPs act in a dose and time dependent manner in which early reduction of BMPs result in digit loss, while inhibiting overall BMP signaling between E10.5 and E11.5 allows polydactylous digit formation. During this period, inhibiting BMPs extends the duration of FGF signaling. Sox9 is initially expressed in normal digit ray domains but at reduced levels that correlate with the reduction in BMP signaling. The persistence of elevated FGF signaling likely promotes cell proliferation and survival, inhibiting the activation of Sox9 and secondarily, inhibiting the differentiation of Sox9-expressing chondrocytes. Our results provide new insights into the timing and clarify the mechanisms underlying BMP signaling during digit morphogenesis.
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Affiliation(s)
- Jacqueline L Norrie
- Department of Molecular Biosciences, Institute for Cellular and Molecular Biology, University of Texas at Austin, 2500 Speedway Stop A4800, Austin, TX 78712, USA
| | - Jordan P Lewandowski
- Department of Molecular Biosciences, Institute for Cellular and Molecular Biology, University of Texas at Austin, 2500 Speedway Stop A4800, Austin, TX 78712, USA
| | - Cortney M Bouldin
- Department of Molecular Genetics and Microbiology, College of Medicine, UF Genetics Institute, 2033 Mowry Road, Gainesville, Florida 32610, USA
| | - Smita Amarnath
- Department of Molecular Biosciences, Institute for Cellular and Molecular Biology, University of Texas at Austin, 2500 Speedway Stop A4800, Austin, TX 78712, USA
| | - Qiang Li
- Department of Molecular Biosciences, Institute for Cellular and Molecular Biology, University of Texas at Austin, 2500 Speedway Stop A4800, Austin, TX 78712, USA
| | - Martha S Vokes
- Department of Molecular Biosciences, Institute for Cellular and Molecular Biology, University of Texas at Austin, 2500 Speedway Stop A4800, Austin, TX 78712, USA
| | - Lauren I R Ehrlich
- Department of Molecular Biosciences, Institute for Cellular and Molecular Biology, University of Texas at Austin, 2500 Speedway Stop A4800, Austin, TX 78712, USA
| | - Brian D Harfe
- Department of Molecular Genetics and Microbiology, College of Medicine, UF Genetics Institute, 2033 Mowry Road, Gainesville, Florida 32610, USA
| | - Steven A Vokes
- Department of Molecular Biosciences, Institute for Cellular and Molecular Biology, University of Texas at Austin, 2500 Speedway Stop A4800, Austin, TX 78712, USA.
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Wang S, Ward J, Leyffer S, Wild SM, Jacobsen C, Vogt S. Unsupervised cell identification on multidimensional X-ray fluorescence datasets. JOURNAL OF SYNCHROTRON RADIATION 2014; 21:568-579. [PMID: 24763647 DOI: 10.1107/s1600577514001416] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2013] [Accepted: 02/17/2014] [Indexed: 06/03/2023]
Abstract
A novel approach to locate, identify and refine positions and whole areas of cell structures based on elemental contents measured by X-ray fluorescence microscopy is introduced. It is shown that, by initializing with only a handful of prototypical cell regions, this approach can obtain consistent identification of whole cells, even when cells are overlapping, without training by explicit annotation. It is robust both to different measurements on the same sample and to different initializations. This effort provides a versatile framework to identify targeted cellular structures from datasets too complex for manual analysis, like most X-ray fluorescence microscopy data. Possible future extensions are also discussed.
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Affiliation(s)
- Siwei Wang
- Mathematics and Computer Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA
| | - Jesse Ward
- Advanced Photon Source, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA
| | - Sven Leyffer
- Mathematics and Computer Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA
| | - Stefan M Wild
- Mathematics and Computer Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA
| | - Chris Jacobsen
- Advanced Photon Source, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA
| | - Stefan Vogt
- Advanced Photon Source, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA
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Chatterjee A. Glutamate-based magnetic resonance spectroscopy in neuroleptic malignant syndrome. Ann Indian Acad Neurol 2014; 17:123-4. [PMID: 24753679 PMCID: PMC3992752 DOI: 10.4103/0972-2327.128579] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2013] [Revised: 03/10/2013] [Accepted: 03/27/2013] [Indexed: 11/17/2022] Open
Abstract
Glutamate neurotoxicity is implicated in a number of neurological diseases, including Neuroleptic Malignant syndrome. Therefore, functional magnetic resonance imaging can help in diagnosis and monitoring such conditions. However, reports of this application are scarce in the literature. In this manuscript, glutamate based imaging of the basal ganglia showed increased levels of the neurotransmitter bilaterally. In addition, a radon transform of the functional image was performed to look for any asymmetry in cerebral activation. Although no asymmetry was detected in this case, this novel analysis can be applied in physiological and pathological scenarios to visualize contribution of different brain structures.
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Affiliation(s)
- Atri Chatterjee
- Department of Medicine, Nil Ratan Sircar Medical College, Kolkata, West Bengal, India
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Codeluppi S, Fernandez-Zafra T, Sandor K, Kjell J, Liu Q, Abrams M, Olson L, Gray NS, Svensson CI, Uhlén P. Interleukin-6 secretion by astrocytes is dynamically regulated by PI3K-mTOR-calcium signaling. PLoS One 2014; 9:e92649. [PMID: 24667246 PMCID: PMC3965459 DOI: 10.1371/journal.pone.0092649] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2013] [Accepted: 02/25/2014] [Indexed: 12/22/2022] Open
Abstract
After contusion spinal cord injury (SCI), astrocytes become reactive and form a glial scar. While this reduces spreading of the damage by containing the area of injury, it inhibits regeneration. One strategy to improve the recovery after SCI is therefore to reduce the inhibitory effect of the scar, once the acute phase of the injury has passed. The pleiotropic cytokine interleukin-6 (IL-6) is secreted immediately after injury and regulates scar formation; however, little is known about the role of IL-6 in the sub-acute phases of SCI. Interestingly, IL-6 also promotes axon regeneration, and therefore its induction in reactive astrocytes may improve regeneration after SCI. We found that IL-6 is expressed by astrocytes and neurons one week post-injury and then declines. Using primary cultures of rat astrocytes we delineated the molecular mechanisms that regulate IL-6 expression and secretion. IL-6 expression requires activation of p38 and depends on NF-κB transcriptional activity. Activation of these pathways in astrocytes occurs when the PI3K-mTOR-AKT pathway is inhibited. Furthermore, we found that an increase in cytosolic calcium concentration was necessary for IL-6 secretion. To induce IL-6 secretion in astrocytes, we used torin2 and rapamycin to block the PI3K-mTOR pathway and increase cytosolic calcium, respectively. Treating injured animals with torin2 and rapamycin for two weeks, starting two weeks after injury when the scar has been formed, lead to a modest effect on mechanical hypersensitivity, limited to the period of treatment. These data, taken together, suggest that treatment with torin2 and rapamycin induces IL-6 secretion by astrocytes and may contribute to the reduction of mechanical hypersensitivity after SCI.
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Affiliation(s)
- Simone Codeluppi
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
- * E-mail:
| | - Teresa Fernandez-Zafra
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Katalin Sandor
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Jacob Kjell
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Qingsong Liu
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Mathew Abrams
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Lars Olson
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Nathanael S. Gray
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Camilla I. Svensson
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Per Uhlén
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
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Candia J, Banavar JR, Losert W. Understanding health and disease with multidimensional single-cell methods. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2014; 26:073102. [PMID: 24451406 PMCID: PMC4020281 DOI: 10.1088/0953-8984/26/7/073102] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Current efforts in the biomedical sciences and related interdisciplinary fields are focused on gaining a molecular understanding of health and disease, which is a problem of daunting complexity that spans many orders of magnitude in characteristic length scales, from small molecules that regulate cell function to cell ensembles that form tissues and organs working together as an organism. In order to uncover the molecular nature of the emergent properties of a cell, it is essential to measure multiple-cell components simultaneously in the same cell. In turn, cell heterogeneity requires multiple-cells to be measured in order to understand health and disease in the organism. This review summarizes current efforts towards a data-driven framework that leverages single-cell technologies to build robust signatures of healthy and diseased phenotypes. While some approaches focus on multicolor flow cytometry data and other methods are designed to analyze high-content image-based screens, we emphasize the so-called Supercell/SVM paradigm (recently developed by the authors of this review and collaborators) as a unified framework that captures mesoscopic-scale emergence to build reliable phenotypes. Beyond their specific contributions to basic and translational biomedical research, these efforts illustrate, from a larger perspective, the powerful synergy that might be achieved from bringing together methods and ideas from statistical physics, data mining, and mathematics to solve the most pressing problems currently facing the life sciences.
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Affiliation(s)
- Julián Candia
- Department of Physics, University of Maryland, College Park, MD 20742, USA. School of Medicine, University of Maryland, Baltimore, MD 21201, USA. IFLYSIB and CONICET, University of La Plata, 1900 La Plata, Argentina
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Pruteanu-Malinici I, Majoros WH, Ohler U. Automated annotation of gene expression image sequences via non-parametric factor analysis and conditional random fields. Bioinformatics 2013; 29:i27-35. [PMID: 23812993 PMCID: PMC3694682 DOI: 10.1093/bioinformatics/btt206] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Motivation: Computational approaches for the annotation of phenotypes from image data have shown promising results across many applications, and provide rich and valuable information for studying gene function and interactions. While data are often available both at high spatial resolution and across multiple time points, phenotypes are frequently annotated independently, for individual time points only. In particular, for the analysis of developmental gene expression patterns, it is biologically sensible when images across multiple time points are jointly accounted for, such that spatial and temporal dependencies are captured simultaneously. Methods: We describe a discriminative undirected graphical model to label gene-expression time-series image data, with an efficient training and decoding method based on the junction tree algorithm. The approach is based on an effective feature selection technique, consisting of a non-parametric sparse Bayesian factor analysis model. The result is a flexible framework, which can handle large-scale data with noisy incomplete samples, i.e. it can tolerate data missing from individual time points. Results: Using the annotation of gene expression patterns across stages of Drosophila embryonic development as an example, we demonstrate that our method achieves superior accuracy, gained by jointly annotating phenotype sequences, when compared with previous models that annotate each stage in isolation. The experimental results on missing data indicate that our joint learning method successfully annotates genes for which no expression data are available for one or more stages. Contact: uwe.ohler@duke.edu
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Multiplex cytological profiling assay to measure diverse cellular states. PLoS One 2013; 8:e80999. [PMID: 24312513 PMCID: PMC3847047 DOI: 10.1371/journal.pone.0080999] [Citation(s) in RCA: 179] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2013] [Accepted: 10/08/2013] [Indexed: 01/18/2023] Open
Abstract
Computational methods for image-based profiling are under active development, but their success hinges on assays that can capture a wide range of phenotypes. We have developed a multiplex cytological profiling assay that “paints the cell” with as many fluorescent markers as possible without compromising our ability to extract rich, quantitative profiles in high throughput. The assay detects seven major cellular components. In a pilot screen of bioactive compounds, the assay detected a range of cellular phenotypes and it clustered compounds with similar annotated protein targets or chemical structure based on cytological profiles. The results demonstrate that the assay captures subtle patterns in the combination of morphological labels, thereby detecting the effects of chemical compounds even though their targets are not stained directly. This image-based assay provides an unbiased approach to characterize compound- and disease-associated cell states to support future probe discovery.
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Doan-Xuan QM, Sarvari AK, Fischer-Posovszky P, Wabitsch M, Balajthy Z, Fesus L, Bacso Z. High content analysis of differentiation and cell death in human adipocytes. Cytometry A 2013; 83:933-43. [PMID: 23846866 DOI: 10.1002/cyto.a.22333] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2013] [Revised: 06/05/2013] [Accepted: 06/10/2013] [Indexed: 12/18/2022]
Abstract
Understanding adipocyte biology and its homeostasis is in the focus of current obesity research. We aimed to introduce a high-content analysis procedure for directly visualizing and quantifying adipogenesis and adipoapoptosis by laser scanning cytometry (LSC) in a large population of cell. Slide-based image cytometry and image processing algorithms were used and optimized for high-throughput analysis of differentiating cells and apoptotic processes in cell culture at high confluence. Both preadipocytes and adipocytes were simultaneously scrutinized for lipid accumulation, texture properties, nuclear condensation, and DNA fragmentation. Adipocyte commitment was found after incubation in adipogenic medium for 3 days identified by lipid droplet formation and increased light absorption, while terminal differentiation of adipocytes occurred throughout day 9-14 with characteristic nuclear shrinkage, eccentric nuclei localization, chromatin condensation, and massive lipid deposition. Preadipocytes were shown to be more prone to tumor necrosis factor alpha (TNFα)-induced apoptosis compared to mature adipocytes. Importantly, spontaneous DNA fragmentation was observed at early stage when adipocyte commitment occurs. This DNA damage was independent from either spontaneous or induced apoptosis and probably was part of the differentiation program. © 2013 International Society for Advancement of Cytometry.
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Affiliation(s)
- Quang Minh Doan-Xuan
- Department of Biophysics and Cell Biology, Medical and Health Science Center, University of Debrecen, Debrecen, Hungary
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