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Nuernberg E, Bruch R, Hafner M, Rudolf R, Vitacolonna M. Quantitative Analysis of Whole-Mount Fluorescence-Stained Tumor Spheroids in Phenotypic Drug Screens. Methods Mol Biol 2024; 2764:311-334. [PMID: 38393603 DOI: 10.1007/978-1-0716-3674-9_20] [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: 02/25/2024]
Abstract
Three-dimensional cell cultures, such as spheroids or organoids, serve as important models for drug screening purposes. Optical tissue clearing (OTC) enhances the visualization of fluorescence stainings and enables in toto microscopy of 3D cell culture models. Furthermore, subsequent automated image analysis tools convert qualitative confocal image sets into quantitative data. In this chapter, we describe a detailed protocol for preparation of HT29 cancer spheroids, 3D in toto immunostaining, glycerol-based OTC, whole-mount imaging, and semi-automated downstream image processing and segmentation for nuclear image analysis using open-source software.
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Affiliation(s)
- Elina Nuernberg
- Institute of Molecular and Cell Biology, Mannheim University of Applied Sciences, Mannheim, Germany
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany
| | - Roman Bruch
- Institute of Molecular and Cell Biology, Mannheim University of Applied Sciences, Mannheim, Germany
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, Germany
| | - Mathias Hafner
- Institute of Molecular and Cell Biology, Mannheim University of Applied Sciences, Mannheim, Germany
| | - Ruediger Rudolf
- Institute of Molecular and Cell Biology, Mannheim University of Applied Sciences, Mannheim, Germany
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany
| | - Mario Vitacolonna
- Institute of Molecular and Cell Biology, Mannheim University of Applied Sciences, Mannheim, Germany.
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany.
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2
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Stossi F, Singh PK, Safari K, Marini M, Labate D, Mancini MA. High throughput microscopy and single cell phenotypic image-based analysis in toxicology and drug discovery. Biochem Pharmacol 2023; 216:115770. [PMID: 37660829 DOI: 10.1016/j.bcp.2023.115770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 08/23/2023] [Accepted: 08/25/2023] [Indexed: 09/05/2023]
Abstract
Measuring single cell responses to the universe of chemicals (drugs, natural products, environmental toxicants etc.) is of paramount importance to human health as phenotypic variability in sensing stimuli is a hallmark of biology that is considered during high throughput screening. One of the ways to approach this problem is via high throughput, microscopy-based assays coupled with multi-dimensional single cell analysis methods. Here, we will summarize some of the efforts in this vast and growing field, focusing on phenotypic screens (e.g., Cell Painting), single cell analytics and quality control, with particular attention to environmental toxicology and drug screening. We will discuss advantages and limitations of high throughput assays with various end points and levels of complexity.
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Affiliation(s)
- Fabio Stossi
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA; GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA.
| | - Pankaj K Singh
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Kazem Safari
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Michela Marini
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Department of Mathematics, University of Houston, Houston, TX, USA
| | - Demetrio Labate
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Department of Mathematics, University of Houston, Houston, TX, USA
| | - Michael A Mancini
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA; GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
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3
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Potts C, Schearer J, Sebrell TA, Bair D, Ayler B, Love J, Dankoff J, Harris PR, Zosso D, Bimczok D. MNPmApp: An image analysis tool to quantify mononuclear phagocyte distribution in mucosal tissues. Cytometry A 2022; 101:1012-1026. [PMID: 35569131 PMCID: PMC9663762 DOI: 10.1002/cyto.a.24657] [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: 06/22/2021] [Revised: 03/27/2022] [Accepted: 05/12/2022] [Indexed: 01/27/2023]
Abstract
Mononuclear phagocytes (MNPs) such as dendritic cells and macrophages perform key sentinel functions in mucosal tissues and are responsible for inducing and maintaining adaptive immune responses to mucosal pathogens. Positioning of MNPs at the epithelial interface facilitates their access to luminally-derived antigens and regulates MNP function through soluble mediators or surface receptor interactions. Therefore, accurately quantifying the distribution of MNPs within mucosal tissues as well as their spatial relationship with other cells is important to infer functional cellular interactions in health and disease. In this study, we developed and validated a MATLAB-based tissue cytometry platform, termed "MNP mapping application" (MNPmApp), that performs high throughput analyses of MNP density and distribution in the gastrointestinal mucosa based on digital multicolor fluorescence microscopy images and that integrates a Monte Carlo modeling feature to assess randomness of MNP distribution. MNPmApp identified MNPs in tissue sections of the human gastric mucosa with 98 ± 2% specificity and 76 ± 15% sensitivity for HLA-DR+ MNPs and 98 ± 1% specificity and 85 ± 12% sensitivity for CD11c+ MNPs. Monte Carlo modeling revealed that mean MNP-MNP distances for both HLA-DR+ and CD11c+ MNPs were significantly lower than anticipated based on random cell placement, whereas MNP-epithelial distances were similar to randomly placed cells. Surprisingly, H. pylori infection had no significant impact on the number of HLA-DR and CD11c MNPs or their distribution within the gastric lamina propria. However, our study demonstrated that MNPmApp is a reliable and user-friendly tool for unbiased quantitation of MNPs and their distribution at mucosal sites.
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Affiliation(s)
- Catherine Potts
- Department of Mathematical Sciences, Montana State University, Bozeman, MT
| | - Julia Schearer
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT
| | - Thomas A Sebrell
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT
| | - Dominic Bair
- Department of Mathematical Sciences, Montana State University, Bozeman, MT
| | | | - Jordan Love
- Department of Mathematical Sciences, Montana State University, Bozeman, MT
| | - Jennifer Dankoff
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT
| | - Paul R. Harris
- Division of Pediatrics, Department of Pediatric Gastroenterology and Nutrition, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Dominique Zosso
- Department of Mathematical Sciences, Montana State University, Bozeman, MT
| | - Diane Bimczok
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT
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4
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A Temporal Evolution Perspective of Lipase Production by Yarrowia lipolytica in Solid-State Fermentation. Processes (Basel) 2022. [DOI: 10.3390/pr10020381] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Lipases are enzymes that, in aqueous or non-aqueous media, act on water-insoluble substrates, mainly catalyzing reactions on carboxyl ester bonds, such as hydrolysis, aminolysis, and (trans)esterification. Yarrowia lipolytica is a non-conventional yeast known for secreting lipases and other bioproducts; therefore, it is of great interest in various industrial fields. The production of lipases can be carried on solid-state fermentation (SSF) that utilizes solid substrates in the absence, or near absence, of free water and presents minimal problems with microbial contamination due to the low water contents in the medium. Moreover, SSF offers high volumetric productivity, targets concentrated compounds, high substrate concentration tolerance, and has less wastewater generation. In this sense, the present work provides a temporal evolution perspective regarding the main aspects of lipase production in SSF by Y. lipolytica, focusing on the most relevant aspects and presenting the potential of such an approach.
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5
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Balluet M, Sizaire F, El Habouz Y, Walter T, Pont J, Giroux B, Bouchareb O, Tramier M, Pecreaux J. Neural network fast-classifies biological images through features selecting to power automated microscopy. J Microsc 2021; 285:3-19. [PMID: 34623634 DOI: 10.1111/jmi.13062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 09/28/2021] [Indexed: 11/26/2022]
Abstract
Artificial intelligence is nowadays used for cell detection and classification in optical microscopy during post-acquisition analysis. The microscopes are now fully automated and next expected to be smart by making acquisition decisions based on the images. It calls for analysing them on the fly. Biology further imposes training on a reduced data set due to cost and time to prepare the samples and have the data sets annotated by experts. We propose a real-time image processing compliant with these specifications by balancing accurate detection and execution performance. We characterised the images using a generic, high-dimensional feature extractor. We then classified the images using machine learning to understand the contribution of each feature in decision and execution time. We found that the non-linear-classifier random forests outperformed Fisher's linear discriminant. More importantly, the most discriminant and time-consuming features could be excluded without significant accuracy loss, offering a substantial gain in execution time. It suggests a feature-group redundancy likely related to the biology of the observed cells. We offer a method to select fast and discriminant features. In our assay, a 79.6 ± 2.4% accurate classification of a cell took 68.7 ± 3.5 ms (mean ± SD, 5-fold cross-validation nested in 10 bootstrap repeats), corresponding to 14 cells per second, dispatched into eight phases of the cell cycle, using 12 feature groups and operating a consumer market ARM-based embedded system. A simple neural network offered similar performances paving the way to faster training and classification, using parallel execution on a general-purpose graphic processing unit. Finally, this strategy is also usable for deep neural networks paving the way to optimizing these algorithms for smart microscopy.
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Affiliation(s)
- Maël Balluet
- CNRS, Univ Rennes, IGDR - UMR 6290, Rennes, France.,Inscoper SAS, Cesson-Sévigné, France
| | - Florian Sizaire
- CNRS, Univ Rennes, IGDR - UMR 6290, Rennes, France.,Present address Biologics Research, Sanofi R&D, Vitry-sur-Seine, France
| | | | - Thomas Walter
- Centre for Computational Biology (CBIO), MINES ParisTech, PSL University, Paris, France.,Institut Curie, Paris, France.,INSERM, U900, Paris, France
| | | | | | | | - Marc Tramier
- CNRS, Univ Rennes, IGDR - UMR 6290, Rennes, France.,Univ Rennes, BIOSIT, UMS CNRS 3480, US INSERM 018, Rennes, France
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6
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Paul-Gilloteaux P, Tosi S, Hériché JK, Gaignard A, Ménager H, Marée R, Baecker V, Klemm A, Kalaš M, Zhang C, Miura K, Colombelli J. Bioimage analysis workflows: community resources to navigate through a complex ecosystem. F1000Res 2021; 10:320. [PMID: 34136134 PMCID: PMC8182692 DOI: 10.12688/f1000research.52569.1] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/14/2021] [Indexed: 11/20/2022] Open
Abstract
Workflows are the keystone of bioimage analysis, and the NEUBIAS (Network of European BioImage AnalystS) community is trying to gather the actors of this field and organize the information around them. One of its most recent outputs is the opening of the F1000Research NEUBIAS gateway, whose main objective is to offer a channel of publication for bioimage analysis workflows and associated resources. In this paper we want to express some personal opinions and recommendations related to finding, handling and developing bioimage analysis workflows. The emergence of "big data" in bioimaging and resource-intensive analysis algorithms make local data storage and computing solutions a limiting factor. At the same time, the need for data sharing with collaborators and a general shift towards remote work, have created new challenges and avenues for the execution and sharing of bioimage analysis workflows. These challenges are to reproducibly run workflows in remote environments, in particular when their components come from different software packages, but also to document them and link their parameters and results by following the FAIR principles (Findable, Accessible, Interoperable, Reusable) to foster open and reproducible science. In this opinion paper, we focus on giving some directions to the reader to tackle these challenges and navigate through this complex ecosystem, in order to find and use workflows, and to compare workflows addressing the same problem. We also discuss tools to run workflows in the cloud and on High Performance Computing resources, and suggest ways to make these workflows FAIR.
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Affiliation(s)
- Perrine Paul-Gilloteaux
- Université de Nantes, CNRS, INSERM, l’institut du thorax, Nantes, F-44000, France
- Université de Nantes, CHU Nantes, Inserm, CNRS, SFR Santé, Inserm UMS 016, CNRS UMS 3556, Nantes, F-44000, France
| | - Sébastien Tosi
- Institute for Research in Biomedicine, IRB Barcelona, Barcelona Institute of Science and Technology, BIST, Barcelona, Spain
| | - Jean-Karim Hériché
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, 69117, Germany
| | - Alban Gaignard
- Université de Nantes, CNRS, INSERM, l’institut du thorax, Nantes, F-44000, France
| | - Hervé Ménager
- Hub de Bioinformatique et Biostatistique, Département Biologie Computationnelle, Institut Pasteur, USR 3756, CNRS, Paris, 75015, France
- CNRS, UMS 3601, Institut Français de Bioinformatique, IFB-core, Evry, 91000, France
| | - Raphaël Marée
- Montefiore Institute, University of Liège, Liège, Belgium
| | - Volker Baecker
- Montpellier Ressources Imagerie, BioCampus Montpellier, CNRS, INSERM, University of Montpellier, Montpellier, F-34000, France
| | - Anna Klemm
- BioImage Informatics Facility, SciLifeLab, Stockholm, Sweden
| | - Matúš Kalaš
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Chong Zhang
- Department of Information and Communication Technologies, University Pompeu Fabra, Barcelona, Spain
| | - Kota Miura
- Nikon Imaging Center, University of Heidelberg, Heidelberg, Germany
| | - Julien Colombelli
- Institute for Research in Biomedicine, IRB Barcelona, Barcelona Institute of Science and Technology, BIST, Barcelona, Spain
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7
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Varsou DD, Afantitis A, Tsoumanis A, Papadiamantis A, Valsami-Jones E, Lynch I, Melagraki G. Zeta-Potential Read-Across Model Utilizing Nanodescriptors Extracted via the NanoXtract Image Analysis Tool Available on the Enalos Nanoinformatics Cloud Platform. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2020; 16:e1906588. [PMID: 32174008 DOI: 10.1002/smll.201906588] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 02/07/2020] [Indexed: 06/10/2023]
Abstract
Zeta potential is one of the most critical properties of nanomaterials (NMs) which provides an estimation of the surface charge, and therefore electrostatic stability in medium and, in practical terms, influences the NM's tendency to form agglomerates and to interact with cellular membranes. This paper describes a robust and accurate read-across model to predict NM zeta potential utilizing as the input data a set of image descriptors derived from transmission electron microscopy (TEM) images of the NMs. The image descriptors are calculated using NanoXtract (http://enaloscloud.novamechanics.com/EnalosWebApps/NanoXtract/), a unique online tool that generates 18 image descriptors from the TEM images, which can then be explored by modeling to identify those most predictive of NM behavior and biological effects. NM TEM images are used to develop a model for prediction of zeta potential based on grouping of the NMs according to their nearest neighbors. The model provides interesting insights regarding the most important similarity features between NMs-in addition to core composition the main elongation emerged, which links to key drivers of NM toxicity such as aspect ratio. Both the NanoXtract image analysis tool and the validated model for zeta potential (http://enaloscloud.novamechanics.com/EnalosWebApps/ZetaPotential/) are freely available online through the Enalos Nanoinformatics platform.
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Affiliation(s)
- Dimitra-Danai Varsou
- Nanoinformatics Department, NovaMechanics Ltd., Nicosia, 1065, Cyprus
- School of Chemical Engineering, National Technical University of Athens, Athens, 15780, Greece
| | - Antreas Afantitis
- Nanoinformatics Department, NovaMechanics Ltd., Nicosia, 1065, Cyprus
| | - Andreas Tsoumanis
- Nanoinformatics Department, NovaMechanics Ltd., Nicosia, 1065, Cyprus
| | - Anastasios Papadiamantis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B152TT, Birmingham, UK
| | - Eugenia Valsami-Jones
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B152TT, Birmingham, UK
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B152TT, Birmingham, UK
| | - Georgia Melagraki
- Nanoinformatics Department, NovaMechanics Ltd., Nicosia, 1065, Cyprus
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8
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Dietz C, Rueden CT, Helfrich S, Dobson ETA, Horn M, Eglinger J, Evans EL, McLean DT, Novitskaya T, Ricke WA, Sherer NM, Zijlstra A, Berthold MR, Eliceiri KW. Integration of the ImageJ Ecosystem in the KNIME Analytics Platform. FRONTIERS IN COMPUTER SCIENCE 2020; 2. [PMID: 32905440 DOI: 10.3389/fcomp.2020.00008] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Open-source software tools are often used for analysis of scientific image data due to their flexibility and transparency in dealing with rapidly evolving imaging technologies. The complex nature of image analysis problems frequently requires many tools to be used in conjunction, including image processing and analysis, data processing, machine learning and deep learning, statistical analysis of the results, visualization, correlation to heterogeneous but related data, and more. However, the development, and therefore application, of these computational tools is impeded by a lack of integration across platforms. Integration of tools goes beyond convenience, as it is impractical for one tool to anticipate and accommodate the current and future needs of every user. This problem is emphasized in the field of bioimage analysis, where various rapidly emerging methods are quickly being adopted by researchers. ImageJ is a popular open-source image analysis platform, with contributions from a global community resulting in hundreds of specialized routines for a wide array of scientific tasks. ImageJ's strength lies in its accessibility and extensibility, allowing researchers to easily improve the software to solve their image analysis tasks. However, ImageJ is not designed for development of complex end-to-end image analysis workflows. Scientists are often forced to create highly specialized and hard-to-reproduce scripts to orchestrate individual software fragments and cover the entire life-cycle of an analysis of an image dataset. KNIME Analytics Platform, a user-friendly data integration, analysis, and exploration workflow system, was designed to handle huge amounts of heterogeneous data in a platform-agnostic, computing environment and has been successful in meeting complex end-to-end demands in several communities, such as cheminformatics and mass spectrometry. Similar needs within the bioimage analysis community led to the creation of the KNIME Image Processing extension which integrates ImageJ into KNIME Analytics Platform, enabling researchers to develop reproducible and scalable workflows, integrating a diverse range of analysis tools. Here we present how users and developers alike can leverage the ImageJ ecosystem via the KNIME Image Processing extension to provide robust and extensible image analysis within KNIME workflows. We illustrate the benefits of this integration with examples, as well as representative scientific use cases.
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Affiliation(s)
| | - Curtis T Rueden
- Laboratory for Optical and Computational Instrumentation (LOCI), Laboratory of Cell and Molecular Biology, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Ellen T A Dobson
- Laboratory for Optical and Computational Instrumentation (LOCI), Laboratory of Cell and Molecular Biology, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Jan Eglinger
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Edward L Evans
- McArdle Laboratory for Cancer Research, Institute for Molecular Virology, and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Dalton T McLean
- George M. O'Brien Center of Research Excellence, University of Wisconsin Madison, WI, USA
| | - Tatiana Novitskaya
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - William A Ricke
- George M. O'Brien Center of Research Excellence, University of Wisconsin Madison, WI, USA
| | - Nathan M Sherer
- McArdle Laboratory for Cancer Research, Institute for Molecular Virology, and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Andries Zijlstra
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Michael R Berthold
- KNIME GmbH, Konstanz, Germany.,University of Konstanz, Konstanz, Germany
| | - Kevin W Eliceiri
- Laboratory for Optical and Computational Instrumentation (LOCI), Laboratory of Cell and Molecular Biology, University of Wisconsin-Madison, Madison, WI, USA.,Morgridge Institute for Research, Madison, WI, USA
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9
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Föll MC, Moritz L, Wollmann T, Stillger MN, Vockert N, Werner M, Bronsert P, Rohr K, Grüning BA, Schilling O. Accessible and reproducible mass spectrometry imaging data analysis in Galaxy. Gigascience 2019; 8:giz143. [PMID: 31816088 PMCID: PMC6901077 DOI: 10.1093/gigascience/giz143] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2019] [Revised: 09/10/2019] [Accepted: 11/10/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Mass spectrometry imaging is increasingly used in biological and translational research because it has the ability to determine the spatial distribution of hundreds of analytes in a sample. Being at the interface of proteomics/metabolomics and imaging, the acquired datasets are large and complex and often analyzed with proprietary software or in-house scripts, which hinders reproducibility. Open source software solutions that enable reproducible data analysis often require programming skills and are therefore not accessible to many mass spectrometry imaging (MSI) researchers. FINDINGS We have integrated 18 dedicated mass spectrometry imaging tools into the Galaxy framework to allow accessible, reproducible, and transparent data analysis. Our tools are based on Cardinal, MALDIquant, and scikit-image and enable all major MSI analysis steps such as quality control, visualization, preprocessing, statistical analysis, and image co-registration. Furthermore, we created hands-on training material for use cases in proteomics and metabolomics. To demonstrate the utility of our tools, we re-analyzed a publicly available N-linked glycan imaging dataset. By providing the entire analysis history online, we highlight how the Galaxy framework fosters transparent and reproducible research. CONCLUSION The Galaxy framework has emerged as a powerful analysis platform for the analysis of MSI data with ease of use and access, together with high levels of reproducibility and transparency.
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Affiliation(s)
- Melanie Christine Föll
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- Faculty of Biology, University of Freiburg, Schänzlestraße 1, 79104 Freiburg, Germany
| | - Lennart Moritz
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
| | - Thomas Wollmann
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Maren Nicole Stillger
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- Faculty of Biology, University of Freiburg, Schänzlestraße 1, 79104 Freiburg, Germany
- Institute of Molecular Medicine and Cell Research, Faculty of Medicine, University of Freiburg, Stefan-Meier-Straße 17, 79104 Freiburg, Germany
| | - Niklas Vockert
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Martin Werner
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- Faculty of Medicine - University of Freiburg, Breisacher Straße 153, 79110 Freiburg, Germany
- Tumorbank Comprehensive Cancer Center Freiburg, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Hugstetter Straße 55, 79106 Freiburg, Germany
| | - Peter Bronsert
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- Faculty of Medicine - University of Freiburg, Breisacher Straße 153, 79110 Freiburg, Germany
- Tumorbank Comprehensive Cancer Center Freiburg, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Hugstetter Straße 55, 79106 Freiburg, Germany
| | - Karl Rohr
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Björn Andreas Grüning
- Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany
| | - Oliver Schilling
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Breisacher Straße 115a, 79106 Freiburg, Germany
- Faculty of Medicine - University of Freiburg, Breisacher Straße 153, 79110 Freiburg, Germany
- German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Hugstetter Straße 55, 79106 Freiburg, Germany
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10
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Wibberg D, Batut B, Belmann P, Blom J, Glöckner FO, Grüning B, Hoffmann N, Kleinbölting N, Rahn R, Rey M, Scholz U, Sharan M, Tauch A, Trojahn U, Usadel B, Kohlbacher O. The de.NBI / ELIXIR-DE training platform - Bioinformatics training in Germany and across Europe within ELIXIR. F1000Res 2019; 8. [PMID: 33163154 PMCID: PMC7607484 DOI: 10.12688/f1000research.20244.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/04/2020] [Indexed: 12/25/2022] Open
Abstract
The German Network for Bioinformatics Infrastructure (de.NBI) is a national and academic infrastructure funded by the German Federal Ministry of Education and Research (BMBF). The de.NBI provides (i) service, (ii) training, and (iii) cloud computing to users in life sciences research and biomedicine in Germany and Europe and (iv) fosters the cooperation of the German bioinformatics community with international network structures. The de.NBI members also run the German node (ELIXIR-DE) within the European ELIXIR infrastructure. The de.NBI / ELIXIR-DE training platform, also known as special interest group 3 (SIG 3) ‘Training & Education’, coordinates the bioinformatics training of de.NBI and the German ELIXIR node. The network provides a high-quality, coherent, timely, and impactful training program across its eight service centers. Life scientists learn how to handle and analyze biological big data more effectively by applying tools, standards and compute services provided by de.NBI. Since 2015, more than 300 training courses were carried out with about 6,000 participants and these courses received recommendation rates of almost 90% (status as of July 2020). In addition to face-to-face training courses, online training was introduced on the de.NBI website in 2016 and guidelines for the preparation of e-learning material were established in 2018. In 2016, ELIXIR-DE joined the ELIXIR training platform. Here, the de.NBI / ELIXIR-DE training platform collaborates with ELIXIR in training activities, advertising training courses via TeSS and discussions on the exchange of data for training events essential for quality assessment on both the technical and administrative levels. The de.NBI training program trained thousands of scientists from Germany and beyond in many different areas of bioinformatics.
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Affiliation(s)
- Daniel Wibberg
- Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, 33501, Germany
| | - Bérénice Batut
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, 79110, Germany
| | - Peter Belmann
- Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, 33501, Germany
| | - Jochen Blom
- Bioinformatics and Systems Biology, Justus-Liebig-University Giessen, Giessen, 35392, Germany
| | - Frank Oliver Glöckner
- Alfred-Wegener-Institut - Helmholtz Zentrum für Polar- und Meeresforschung and Jacobs University Bremen, Campus Ring 1, Bremen, 28759, Germany
| | - Björn Grüning
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, 79110, Germany
| | - Nils Hoffmann
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, 44227, Germany
| | - Nils Kleinbölting
- Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, 33501, Germany
| | - René Rahn
- Algorithmic Bioinformatics, Department of Mathematics and Computer Science, Freie Universität Berlin, Takustraße 9, Berlin, 14195, Germany
| | - Maja Rey
- Scientific Databases and Visualization Group, Heidelberg Institute for Theoretical Studies (HITS) gGmbH, Schloss-Wolfsbrunnenweg 35, Heidelberg, 69118, Germany
| | - Uwe Scholz
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, 06466, Germany
| | - Malvika Sharan
- The Heidelberg Center for Human Bioinformatics (HD-HuB), European Molecular Biology Laboratory, Meyerhofstrasse 1, Heidelberg, 69117, Germany
| | - Andreas Tauch
- Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, 33501, Germany
| | - Ulrike Trojahn
- The Heidelberg Center for Human Bioinformatics (HD-HuB), European Molecular Biology Laboratory, Meyerhofstrasse 1, Heidelberg, 69117, Germany
| | - Björn Usadel
- IBG-2 Plant Sciences, Forschungszentrum Jülich, Jülich, 52428, Germany
| | - Oliver Kohlbacher
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Tübingen, 72076, Germany.,Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, 72076, Germany.,Translational Bioinformatics, University Hospital Tubingen, Tübingen, 72076, Germany.,Biomolecular Interactions, Max Planck Institute for Development Biology, Tübingen, 72076, Germany
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11
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Patel DS, Xu N, Lu H. Digging deeper: methodologies for high-content phenotyping in Caenorhabditis elegans. Lab Anim (NY) 2019; 48:207-216. [PMID: 31217565 DOI: 10.1038/s41684-019-0326-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 05/17/2019] [Indexed: 11/09/2022]
Abstract
Deep phenotyping is an emerging conceptual paradigm and experimental approach aimed at measuring and linking many aspects of a phenotype to understand its underlying biology. To date, deep phenotyping has been applied mostly in cultured cells and used less in multicellular organisms. However, in the past decade, it has increasingly been recognized that deep phenotyping could lead to a better understanding of how genetics, environment and stochasticity affect the development, physiology and behavior of an organism. The nematode Caenorhabditis elegans is an invaluable model system for studying how genes affect a phenotypic trait, and new technologies have taken advantage of the worm's physical attributes to increase the throughput and informational content of experiments. Coupling of these technical advancements with computational and analytical tools has enabled a boom in deep-phenotyping studies of C. elegans. In this Review, we highlight how these new technologies and tools are digging into the biological origins of complex, multidimensional phenotypes.
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Affiliation(s)
- Dhaval S Patel
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Nan Xu
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA.,The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Hang Lu
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
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12
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Ritter C, Wollmann T, Bernhard P, Gunkel M, Braun DM, Lee JY, Meiners J, Simon R, Sauter G, Erfle H, Rippe K, Bartenschlager R, Rohr K. Hyperparameter optimization for image analysis: application to prostate tissue images and live cell data of virus-infected cells. Int J Comput Assist Radiol Surg 2019; 14:1847-1857. [PMID: 31177423 DOI: 10.1007/s11548-019-02010-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2019] [Accepted: 05/30/2019] [Indexed: 11/29/2022]
Abstract
PURPOSE Automated analysis of microscopy image data typically requires complex pipelines that involve multiple methods for different image analysis tasks. To achieve best results of the analysis pipelines, method-dependent hyperparameters need to be optimized. However, complex pipelines often suffer from the fact that calculation of the gradient of the loss function is analytically or computationally infeasible. Therefore, first- or higher-order optimization methods cannot be applied. METHODS We developed a new framework for zero-order black-box hyperparameter optimization called HyperHyper, which has a modular architecture that separates hyperparameter sampling and optimization. We also developed a visualization of the loss function based on infimum projection to obtain further insights into the optimization problem. RESULTS We applied HyperHyper in three different experiments with different imaging modalities, and evaluated in total more than 400.000 hyperparameter combinations. HyperHyper was used for optimizing two pipelines for cell nuclei segmentation in prostate tissue microscopy images and two pipelines for detection of hepatitis C virus proteins in live cell microscopy data. We evaluated the impact of separating the sampling and optimization strategy using different optimizers and employed an infimum projection for visualizing the hyperparameter space. CONCLUSIONS The separation of sampling and optimization strategy of the proposed HyperHyper optimization framework improves the result of the investigated image analysis pipelines. Visualization of the loss function based on infimum projection enables gaining further insights on the optimization process.
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Affiliation(s)
- Christian Ritter
- Biomedical Computer Vision Group, BioQuant, IPMB, University of Heidelberg and DKFZ, Im Neuenheimer Feld 267, Heidelberg, Germany.
| | - Thomas Wollmann
- Biomedical Computer Vision Group, BioQuant, IPMB, University of Heidelberg and DKFZ, Im Neuenheimer Feld 267, Heidelberg, Germany
| | - Patrick Bernhard
- Biomedical Computer Vision Group, BioQuant, IPMB, University of Heidelberg and DKFZ, Im Neuenheimer Feld 267, Heidelberg, Germany
| | - Manuel Gunkel
- High-Content Analysis of the Cell (HiCell) and Advanced Biological Screening Facility, BioQuant, University of Heidelberg, Im Neuenheimer Feld 267, Heidelberg, Germany
| | - Delia M Braun
- Division of Chromatin Networks, DKFZ and BioQuant, Im Neuenheimer Feld 267, Heidelberg, Germany
| | - Ji-Young Lee
- Department of Infectious Diseases, Molecular Virology, University Hospital Heidelberg, Im Neuenheimer Feld 344, Heidelberg, Germany.,German Center of Infection Research, Heidelberg Partner Site, Heidelberg, Germany
| | - Jan Meiners
- Department of Pathology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, Hamburg, Germany
| | - Ronald Simon
- Department of Pathology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, Hamburg, Germany
| | - Guido Sauter
- Department of Pathology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, Hamburg, Germany
| | - Holger Erfle
- High-Content Analysis of the Cell (HiCell) and Advanced Biological Screening Facility, BioQuant, University of Heidelberg, Im Neuenheimer Feld 267, Heidelberg, Germany
| | - Karsten Rippe
- Division of Chromatin Networks, DKFZ and BioQuant, Im Neuenheimer Feld 267, Heidelberg, Germany
| | - Ralf Bartenschlager
- Department of Infectious Diseases, Molecular Virology, University Hospital Heidelberg, Im Neuenheimer Feld 344, Heidelberg, Germany.,German Center of Infection Research, Heidelberg Partner Site, Heidelberg, Germany
| | - Karl Rohr
- Biomedical Computer Vision Group, BioQuant, IPMB, University of Heidelberg and DKFZ, Im Neuenheimer Feld 267, Heidelberg, Germany
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13
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14
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Goodstadt MN, Marti-Renom MA. Communicating Genome Architecture: Biovisualization of the Genome, from Data Analysis and Hypothesis Generation to Communication and Learning. J Mol Biol 2018; 431:1071-1087. [PMID: 30419242 DOI: 10.1016/j.jmb.2018.11.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 10/29/2018] [Accepted: 11/01/2018] [Indexed: 01/07/2023]
Abstract
Genome discoveries at the core of biology are made by visual description and exploration of the cell, from microscopic sketches and biochemical mapping to computational analysis and spatial modeling. We outline the experimental and visualization techniques that have been developed recently which capture the three-dimensional interactions regulating how genes are expressed. We detail the challenges faced in integration of the data to portray the components and organization and their dynamic landscape. The goal is more than a single data-driven representation as interactive visualization for de novo research is paramount to decipher insights on genome organization in space.
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Affiliation(s)
- Mike N Goodstadt
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Baldiri Reixac 4, Barcelona 08028, Spain; Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain.
| | - Marc A Marti-Renom
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Baldiri Reixac 4, Barcelona 08028, Spain; Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluis Companys 23, Barcelona 08010, Spain.
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15
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Rae Buchberger A, DeLaney K, Johnson J, Li L. Mass Spectrometry Imaging: A Review of Emerging Advancements and Future Insights. Anal Chem 2018; 90:240-265. [PMID: 29155564 PMCID: PMC5959842 DOI: 10.1021/acs.analchem.7b04733] [Citation(s) in RCA: 559] [Impact Index Per Article: 93.2] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Amanda Rae Buchberger
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Kellen DeLaney
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Jillian Johnson
- School of Pharmacy, University of Wisconsin-Madison, 777 Highland Avenue, Madison, Wisconsin 53705, United States
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, Wisconsin 53706, United States
- School of Pharmacy, University of Wisconsin-Madison, 777 Highland Avenue, Madison, Wisconsin 53705, United States
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