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Goedhart J. Studentsourcing-Aggregating and reusing data from a practical cell biology course. PLoS Comput Biol 2024; 20:e1011836. [PMID: 38358960 PMCID: PMC10868854 DOI: 10.1371/journal.pcbi.1011836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2024] Open
Abstract
Practical courses mimic experimental research and may generate valuable data. Yet, data that is generated by students during a course is often lost as there is no centrally organized collection and storage of the data. The loss of data prevents its reuse. To provide access to these data, I present an approach that I call studentsourcing. It collects, aggregates, and reuses data that is generated by students in a practical course on cell biology. The course runs annually, and I have recorded the data that was generated by >100 students over 3 years. Two use cases illustrate how the data can be aggregated and reused either for the scientific record or for teaching. As the data is obtained by different students, in different groups, over different years, it is an excellent opportunity to discuss experimental design and modern data visualization methods such as the superplot. The first use case demonstrates how the data can be presented as an online, interactive dashboard, providing real-time data of the measurements. The second use case shows how central data storage provides a unique opportunity to get precise quantitative data due to the large sample size. Both use cases illustrate how data can be effectively aggregated and reused.
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Affiliation(s)
- Joachim Goedhart
- Swammerdam Institute for Life Sciences, Section of Molecular Cytology, van Leeuwenhoek Centre for Advanced Microscopy, University of Amsterdam, Amsterdam, the Netherlands
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2
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IMAGE-IN: Interactive web-based multidimensional 3D visualizer for multi-modal microscopy images. PLoS One 2022; 17:e0279825. [PMID: 36584152 PMCID: PMC9803232 DOI: 10.1371/journal.pone.0279825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 12/14/2022] [Indexed: 12/31/2022] Open
Abstract
Advances in microscopy hardware and storage capabilities lead to increasingly larger multidimensional datasets. The multiple dimensions are commonly associated with space, time, and color channels. Since "seeing is believing", it is important to have easy access to user-friendly visualization software. Here we present IMAGE-IN, an interactive web-based multidimensional (N-D) viewer designed specifically for confocal laser scanning microscopy (CLSM) and focused ion beam scanning electron microscopy (FIB-SEM) data, with the goal of assisting biologists in their visualization and analysis tasks and promoting digital workflows. This new visualization platform includes intuitive multidimensional opacity fine-tuning, shading on/off, multiple blending modes for volume viewers, and the ability to handle multichannel volumetric data in volume and surface views. The software accepts a sequence of image files or stacked 3D images as input and offers a variety of viewing options ranging from 3D volume/surface rendering to multiplanar reconstruction approaches. We evaluate the performance by comparing the loading and rendering timings of a heterogeneous dataset of multichannel CLSM and FIB-SEM images on two devices with installed graphic cards, as well as comparing rendered image quality between ClearVolume (the ImageJ open-source desktop viewer), Napari (the Python desktop viewer), Imaris (the closed-source desktop viewer), and our proposed IMAGE-IN web viewer.
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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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Javer A, Rittscher J, Sailem HZ. DeepScratch: Single-cell based topological metrics of scratch wound assays. Comput Struct Biotechnol J 2020; 18:2501-2509. [PMID: 33005312 PMCID: PMC7516198 DOI: 10.1016/j.csbj.2020.08.018] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Revised: 08/18/2020] [Accepted: 08/21/2020] [Indexed: 12/14/2022] Open
Abstract
Changes in tissue architecture and multicellular organisation contribute to many diseases, including cancer and cardiovascular diseases. Scratch wound assay is a commonly used tool that assesses cells' migratory ability based on the area of a wound they cover over a certain time. However, analysis of changes in the organisational patterns formed by migrating cells following genetic or pharmacological perturbations are not well explored in these assays, in part because analysing the resulting imaging data is challenging. Here we present DeepScratch, a neural network that accurately detects the cells in scratch assays based on a heterogeneous set of markers. We demonstrate the utility of DeepScratch by analysing images of more than 232,000 lymphatic endothelial cells. In addition, we propose various topological measures of cell connectivity and local cell density (LCD) to characterise tissue remodelling during wound healing. We show that LCD-based metrics allow classification of CDH5 and CDC42 genetic perturbations that are known to affect cell migration through different biological mechanisms. Such differences cannot be captured when considering only the wound area. Taken together, single-cell detection using DeepScratch allows more detailed investigation of the roles of various genetic components in tissue topology and the biological mechanisms underlying their effects on collective cell migration.
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Affiliation(s)
- Avelino Javer
- Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus Research Building, University of Oxford OX3 7DQ, UK
| | - Jens Rittscher
- Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus Research Building, University of Oxford OX3 7DQ, UK
- Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Old Road Campus Research Building, Oxford OX3 7LF, UK
| | - Heba Z. Sailem
- Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus Research Building, University of Oxford OX3 7DQ, UK
- Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Old Road Campus Research Building, Oxford OX3 7LF, UK
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Meijering E. A bird's-eye view of deep learning in bioimage analysis. Comput Struct Biotechnol J 2020; 18:2312-2325. [PMID: 32994890 PMCID: PMC7494605 DOI: 10.1016/j.csbj.2020.08.003] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 07/26/2020] [Accepted: 08/01/2020] [Indexed: 02/07/2023] Open
Abstract
Deep learning of artificial neural networks has become the de facto standard approach to solving data analysis problems in virtually all fields of science and engineering. Also in biology and medicine, deep learning technologies are fundamentally transforming how we acquire, process, analyze, and interpret data, with potentially far-reaching consequences for healthcare. In this mini-review, we take a bird's-eye view at the past, present, and future developments of deep learning, starting from science at large, to biomedical imaging, and bioimage analysis in particular.
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Affiliation(s)
- Erik Meijering
- School of Computer Science and Engineering & Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia
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Michelena J, Altmeyer M. Cell Cycle Resolved Measurements of Poly(ADP-Ribose) Formation and DNA Damage Signaling by Quantitative Image-Based Cytometry. Methods Mol Biol 2018; 1608:57-68. [PMID: 28695503 DOI: 10.1007/978-1-4939-6993-7_5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Formation of poly(ADP-ribose) (PAR) marks intracellular stress signaling and is notably induced upon DNA damage. PAR polymerases (PARPs) catalyze PAR synthesis upon genotoxic stress and thereby recruit multiple proteins to damaged chromatin. PAR induction is transient and antagonized by the action of PAR glycohydrolase (PARG). Given that poly(ADP-ribosyl)ation (PARylation) is involved in genome integrity maintenance and other vital cellular functions, but also in light of the recent approval of PARP inhibitors for cancer treatments, reliable measurements of intracellular PAR formation have gained importance. Here we provide a detailed protocol for PAR measurements by quantitative image-based cytometry. This technique combines the high spatial resolution of single-cell microscopy with the advantages of cell population measurements through automated high-content imaging. Such upscaling of immunofluorescence-based PAR detection not only increases the robustness of the measurements through averaging across large cell populations but also allows for the discrimination of subpopulations and thus enables multivariate measurements of PAR levels and DNA damage signaling. We illustrate how this technique can be used to assess the dynamics of the cellular response to oxidative damage as well as to PARP inhibitor-induced genotoxicity in a cell cycle resolved manner. Due to the possibility to use any automated microscope for quantitative image-based cytometry, the presented method has widespread applicability in the area of PARP biology and beyond.
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Affiliation(s)
- Jone Michelena
- Department of Molecular Mechanisms of Disease, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland
| | - Matthias Altmeyer
- Department of Molecular Mechanisms of Disease, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.
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Data-analysis strategies for image-based cell profiling. Nat Methods 2017; 14:849-863. [PMID: 28858338 PMCID: PMC6871000 DOI: 10.1038/nmeth.4397] [Citation(s) in RCA: 375] [Impact Index Per Article: 53.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Accepted: 07/28/2017] [Indexed: 12/16/2022]
Abstract
Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.
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Pascual-Vargas P, Cooper S, Sero J, Bousgouni V, Arias-Garcia M, Bakal C. RNAi screens for Rho GTPase regulators of cell shape and YAP/TAZ localisation in triple negative breast cancer. Sci Data 2017; 4:170018. [PMID: 28248929 PMCID: PMC5332010 DOI: 10.1038/sdata.2017.18] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Accepted: 12/01/2016] [Indexed: 12/19/2022] Open
Abstract
In order to metastasise, triple negative breast cancer (TNBC) must make dynamic changes in cell shape. The shape of all eukaryotic cells is regulated by Rho Guanine Nucleotide Exchange Factors (RhoGEFs), which activate Rho-family GTPases in response to mechanical and informational cues. In contrast, Rho GTPase-activating proteins (RhoGAPs) inhibit Rho GTPases. However, which RhoGEFs and RhoGAPS couple TNBC cell shape to changes in their environment is very poorly understood. Moreover, whether the activity of particular RhoGEFs and RhoGAPs become dysregulated as cells evolve the ability to metastasise is not clear. Towards the ultimate goal of identifying RhoGEFs and RhoGAPs that are essential for TNBC metastasis, we performed an RNAi screen to isolate RhoGEFs and RhoGAPs that contribute to the morphogenesis of the highly metastatic TNBC cell line LM2, and its less-metastatic parental cell line MDA-MB-231. For ~6 million cells from each cell line, we measured 127 different features following the depletion of 142 genes. Using a linear classifier scheme we also describe the morphological heterogeneity of each gene-depleted population.
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Affiliation(s)
- Patricia Pascual-Vargas
- Dynamical Cell Systems Team, Cancer Biology, Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
| | - Samuel Cooper
- Dynamical Cell Systems Team, Cancer Biology, Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
- Department of Computational Systems Medicine, Imperial College London, South Kensington Campus, London SW7, UK
| | - Julia Sero
- Dynamical Cell Systems Team, Cancer Biology, Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
| | - Vicky Bousgouni
- Dynamical Cell Systems Team, Cancer Biology, Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
| | - Mar Arias-Garcia
- Dynamical Cell Systems Team, Cancer Biology, Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
| | - Chris Bakal
- Dynamical Cell Systems Team, Cancer Biology, Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
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9
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An Overview of data science uses in bioimage informatics. Methods 2017; 115:110-118. [DOI: 10.1016/j.ymeth.2016.12.014] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 12/09/2016] [Accepted: 12/30/2016] [Indexed: 01/17/2023] Open
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