151
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PopulationProfiler: A Tool for Population Analysis and Visualization of Image-Based Cell Screening Data. PLoS One 2016; 11:e0151554. [PMID: 26987120 PMCID: PMC4795740 DOI: 10.1371/journal.pone.0151554] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Accepted: 03/01/2016] [Indexed: 12/02/2022] Open
Abstract
Image-based screening typically produces quantitative measurements of cell appearance. Large-scale screens involving tens of thousands of images, each containing hundreds of cells described by hundreds of measurements, result in overwhelming amounts of data. Reducing per-cell measurements to the averages across the image(s) for each treatment leads to loss of potentially valuable information on population variability. We present PopulationProfiler—a new software tool that reduces per-cell measurements to population statistics. The software imports measurements from a simple text file, visualizes population distributions in a compact and comprehensive way, and can create gates for subpopulation classes based on control samples. We validate the tool by showing how PopulationProfiler can be used to analyze the effect of drugs that disturb the cell cycle, and compare the results to those obtained with flow cytometry.
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152
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Levario TJ, Lim B, Shvartsman SY, Lu H. Microfluidics for High-Throughput Quantitative Studies of Early Development. Annu Rev Biomed Eng 2016; 18:285-309. [PMID: 26928208 DOI: 10.1146/annurev-bioeng-100515-013926] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Developmental biology has traditionally relied on qualitative analyses; recently, however, as in other fields of biology, researchers have become increasingly interested in acquiring quantitative knowledge about embryogenesis. Advances in fluorescence microscopy are enabling high-content imaging in live specimens. At the same time, microfluidics and automation technologies are increasing experimental throughput for studies of multicellular models of development. Furthermore, computer vision methods for processing and analyzing bioimage data are now leading the way toward quantitative biology. Here, we review advances in the areas of fluorescence microscopy, microfluidics, and data analysis that are instrumental to performing high-content, high-throughput studies in biology and specifically in development. We discuss a case study of how these techniques have allowed quantitative analysis and modeling of pattern formation in the Drosophila embryo.
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Affiliation(s)
- Thomas J Levario
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332;
| | - Bomyi Lim
- Department of Chemical and Biological Engineering and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544;
| | - Stanislav Y Shvartsman
- Department of Chemical and Biological Engineering and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544;
| | - Hang Lu
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332;
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153
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Singh A, Ganapathysubramanian B, Singh AK, Sarkar S. Machine Learning for High-Throughput Stress Phenotyping in Plants. TRENDS IN PLANT SCIENCE 2016; 21:110-124. [PMID: 26651918 DOI: 10.1016/j.tplants.2015.10.015] [Citation(s) in RCA: 315] [Impact Index Per Article: 39.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Revised: 09/30/2015] [Accepted: 10/21/2015] [Indexed: 05/18/2023]
Abstract
Advances in automated and high-throughput imaging technologies have resulted in a deluge of high-resolution images and sensor data of plants. However, extracting patterns and features from this large corpus of data requires the use of machine learning (ML) tools to enable data assimilation and feature identification for stress phenotyping. Four stages of the decision cycle in plant stress phenotyping and plant breeding activities where different ML approaches can be deployed are (i) identification, (ii) classification, (iii) quantification, and (iv) prediction (ICQP). We provide here a comprehensive overview and user-friendly taxonomy of ML tools to enable the plant community to correctly and easily apply the appropriate ML tools and best-practice guidelines for various biotic and abiotic stress traits.
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Affiliation(s)
- Arti Singh
- Department of Agronomy, Iowa State University, Ames, IA, USA.
| | | | | | - Soumik Sarkar
- Department of Mechanical Engineering, Iowa State University, Ames, IA, USA
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154
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155
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Margineanu MB, Julfakyan K, Sommer C, Perez JE, Contreras MF, Khashab N, Kosel J, Ravasi T. Semi-automated quantification of living cells with internalized nanostructures. J Nanobiotechnology 2016; 14:4. [PMID: 26768888 PMCID: PMC4714438 DOI: 10.1186/s12951-015-0153-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2015] [Accepted: 12/17/2015] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Nanostructures fabricated by different methods have become increasingly important for various applications in biology and medicine, such as agents for medical imaging or cancer therapy. In order to understand their interaction with living cells and their internalization kinetics, several attempts have been made in tagging them. Although methods have been developed to measure the number of nanostructures internalized by the cells, there are only few approaches aimed to measure the number of cells that internalize the nanostructures, and they are usually limited to fixed-cell studies. Flow cytometry can be used for live-cell assays on large populations of cells, however it is a single time point measurement, and does not include any information about cell morphology. To date many of the observations made on internalization events are limited to few time points and cells. RESULTS In this study, we present a method for quantifying cells with internalized magnetic nanowires (NWs). A machine learning-based computational framework, CellCognition, is adapted and used to classify cells with internalized and no internalized NWs, labeled with the fluorogenic pH-dependent dye pHrodo™ Red, and subsequently to determine the percentage of cells with internalized NWs at different time points. In a "proof-of-concept", we performed a study on human colon carcinoma HCT 116 cells and human epithelial cervical cancer HeLa cells interacting with iron (Fe) and nickel (Ni) NWs. CONCLUSIONS This study reports a novel method for the quantification of cells that internalize a specific type of nanostructures. This approach is suitable for high-throughput and real-time data analysis and has the potential to be used to study the interaction of different types of nanostructures in live-cell assays.
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Affiliation(s)
- Michael Bogdan Margineanu
- Division of Biological and Environmental Sciences and Engineering, KAUST Environmental Epigenetic Program (KEEP), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Kingdom of Saudi Arabia. .,Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia.
| | - Khachatur Julfakyan
- Division of Physical Science and Engineering, Smart Hybrid Materials Laboratory (SHMs), King Abdullah University of Science and Technology,, Thuwal, Kingdom of Saudi Arabia.
| | - Christoph Sommer
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Dr. Bohr-Gasse 3, Vienna, 1030, Austria.
| | - Jose Efrain Perez
- Division of Biological and Environmental Sciences and Engineering, KAUST Environmental Epigenetic Program (KEEP), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Kingdom of Saudi Arabia. .,Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia.
| | - Maria Fernanda Contreras
- Division of Biological and Environmental Sciences and Engineering, KAUST Environmental Epigenetic Program (KEEP), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Kingdom of Saudi Arabia.
| | - Niveen Khashab
- Division of Physical Science and Engineering, Smart Hybrid Materials Laboratory (SHMs), King Abdullah University of Science and Technology,, Thuwal, Kingdom of Saudi Arabia.
| | - Jürgen Kosel
- Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia.
| | - Timothy Ravasi
- Division of Biological and Environmental Sciences and Engineering, KAUST Environmental Epigenetic Program (KEEP), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Kingdom of Saudi Arabia. .,Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia.
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156
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Belevich I, Joensuu M, Kumar D, Vihinen H, Jokitalo E. Microscopy Image Browser: A Platform for Segmentation and Analysis of Multidimensional Datasets. PLoS Biol 2016; 14:e1002340. [PMID: 26727152 PMCID: PMC4699692 DOI: 10.1371/journal.pbio.1002340] [Citation(s) in RCA: 241] [Impact Index Per Article: 30.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Understanding the structure-function relationship of cells and organelles in their natural context requires multidimensional imaging. As techniques for multimodal 3-D imaging have become more accessible, effective processing, visualization, and analysis of large datasets are posing a bottleneck for the workflow. Here, we present a new software package for high-performance segmentation and image processing of multidimensional datasets that improves and facilitates the full utilization and quantitative analysis of acquired data, which is freely available from a dedicated website. The open-source environment enables modification and insertion of new plug-ins to customize the program for specific needs. We provide practical examples of program features used for processing, segmentation and analysis of light and electron microscopy datasets, and detailed tutorials to enable users to rapidly and thoroughly learn how to use the program.
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Affiliation(s)
- Ilya Belevich
- Electron Microscopy Unit, Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Merja Joensuu
- Electron Microscopy Unit, Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Darshan Kumar
- Electron Microscopy Unit, Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Helena Vihinen
- Electron Microscopy Unit, Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Eija Jokitalo
- Electron Microscopy Unit, Institute of Biotechnology, University of Helsinki, Helsinki, Finland
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157
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Logan DJ, Shan J, Bhatia SN, Carpenter AE. Quantifying co-cultured cell phenotypes in high-throughput using pixel-based classification. Methods 2015; 96:6-11. [PMID: 26687239 DOI: 10.1016/j.ymeth.2015.12.002] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Revised: 12/04/2015] [Accepted: 12/05/2015] [Indexed: 12/21/2022] Open
Abstract
Biologists increasingly use co-culture systems in which two or more cell types are grown in cell culture together in order to better model cells' native microenvironments. Co-cultures are often required for cell survival or proliferation, or to maintain physiological functioning in vitro. Having two cell types co-exist in culture, however, poses several challenges, including difficulties distinguishing the two populations during analysis using automated image analysis algorithms. We previously analyzed co-cultured primary human hepatocytes and mouse fibroblasts in a high-throughput image-based chemical screen, using a combination of segmentation, measurement, and subsequent machine learning to score each cell as hepatocyte or fibroblast. While this approach was successful in counting hepatocytes for primary screening, segmentation of the fibroblast nuclei was less accurate. Here, we present an improved approach that more accurately identifies both cell types. Pixel-based machine learning (using the software ilastik) is used to seed segmentation of each cell type individually (using the software CellProfiler). This streamlined and accurate workflow can be carried out using freely available and open source software.
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Affiliation(s)
- David J Logan
- The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, United States.
| | - Jing Shan
- Harvard-MIT Division of Health Sciences and Technology, MIT, E25-518, 77 Massachusetts Ave, Cambridge, MA 02139, United States.
| | - Sangeeta N Bhatia
- The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, United States; Harvard-MIT Division of Health Sciences and Technology, MIT, E25-518, 77 Massachusetts Ave, Cambridge, MA 02139, United States; Department of Medicine, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115, United States; Institute for Medical Engineering and Science, MIT, E25-330, 77 Massachusetts Ave, Cambridge, MA 02139, United States; Department of Electrical Engineering and Computer Science, MIT, 38-401, 77 Massachusetts Ave, Cambridge, MA 02139, United States; David H. Koch Institute for Integrative Cancer Research, MIT, 76-158, 77 Massachusetts Avenue, Cambridge, MA 02139, United States; Howard Hughes Medical Institute, 4000 Jones Bridge Road, Chevy Chase, MD 20815-6789, United States.
| | - Anne E Carpenter
- The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, United States.
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158
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Abstract
Next-generation sequencing approaches have considerably advanced our understanding of genome function and regulation. However, the knowledge of gene function and complex cellular processes remains a challenge and bottleneck in biological research. Phenomics is a rapidly emerging area, which seeks to rigorously characterize all phenotypes associated with genes or gene variants. Such high-throughput phenotyping under different conditions can be a potent approach toward gene function. The fission yeast Schizosaccharomyces pombe (S. pombe) is a proven eukaryotic model organism that is increasingly used for genomewide screens and phenomic assays. In this review, we highlight current large-scale, cell-based approaches used with S. pombe, including computational colony-growth measurements, genetic interaction screens, parallel profiling using barcodes, microscopy-based cell profiling, metabolomic methods and transposon mutagenesis. These diverse methods are starting to offer rich insights into the relationship between genotypes and phenotypes.
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Affiliation(s)
- Charalampos Rallis
- a Research Department of Genetics , Evolution and Environment and UCL Institute of Healthy Ageing, University College London , London , UK
| | - Jürg Bähler
- a Research Department of Genetics , Evolution and Environment and UCL Institute of Healthy Ageing, University College London , London , UK
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159
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Puah WC, Wasser M. Live imaging of muscles in Drosophila metamorphosis: Towards high-throughput gene identification and function analysis. Methods 2015; 96:103-117. [PMID: 26431669 DOI: 10.1016/j.ymeth.2015.09.028] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Revised: 09/28/2015] [Accepted: 09/29/2015] [Indexed: 12/16/2022] Open
Abstract
Time-lapse microscopy in developmental biology is an emerging tool for functional genomics. Phenotypic effects of gene perturbations can be studied non-invasively at multiple time points in chronological order. During metamorphosis of Drosophila melanogaster, time-lapse microscopy using fluorescent reporters allows visualization of alternative fates of larval muscles, which are a model for the study of genes related to muscle wasting. While doomed muscles enter hormone-induced programmed cell death, a smaller population of persistent muscles survives to adulthood and undergoes morphological remodeling that involves atrophy in early, and hypertrophy in late pupation. We developed a method that combines in vivo imaging, targeted gene perturbation and image analysis to identify and characterize genes involved in muscle development. Macrozoom microscopy helps to screen for interesting muscle phenotypes, while confocal microscopy in multiple locations over 4-5 days produces time-lapse images that are used to quantify changes in cell morphology. Performing a similar investigation using fixed pupal tissues would be too time-consuming and therefore impractical. We describe three applications of our pipeline. First, we show how quantitative microscopy can track and measure morphological changes of muscle throughout metamorphosis and analyze genes involved in atrophy. Second, our assay can help to identify genes that either promote or prevent histolysis of abdominal muscles. Third, we apply our approach to test new fluorescent proteins as live markers for muscle development. We describe mKO2 tagged Cysteine proteinase 1 (Cp1) and Troponin-I (TnI) as examples of proteins showing developmental changes in subcellular localization. Finally, we discuss strategies to improve throughput of our pipeline to permit genome-wide screens in the future.
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Affiliation(s)
- Wee Choo Puah
- Imaging Informatics Division, Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01 Matrix, Singapore 138671, Republic of Singapore.
| | - Martin Wasser
- Imaging Informatics Division, Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01 Matrix, Singapore 138671, Republic of Singapore.
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160
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Nalisnik M, Gutman DA, Kong J, Cooper LAD. An Interactive Learning Framework for Scalable Classification of Pathology Images. PROCEEDINGS : ... IEEE INTERNATIONAL CONFERENCE ON BIG DATA. IEEE INTERNATIONAL CONFERENCE ON BIG DATA 2015; 2015:928-935. [PMID: 27796014 PMCID: PMC5082843 DOI: 10.1109/bigdata.2015.7363841] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Recent advances in microscopy imaging and genomics have created an explosion of patient data in the pathology domain. Whole-slide images (WSIs) of tissues can now capture disease processes as they unfold in high resolution, recording the visual cues that have been the basis of pathologic diagnosis for over a century. Each WSI contains billions of pixels and up to a million or more microanatomic objects whose appearances hold important prognostic information. Computational image analysis enables the mining of massive WSI datasets to extract quantitative morphologic features describing the visual qualities of patient tissues. When combined with genomic and clinical variables, this quantitative information provides scientists and clinicians with insights into disease biology and patient outcomes. To facilitate interaction with this rich resource, we have developed a web-based machine-learning framework that enables users to rapidly build classifiers using an intuitive active learning process that minimizes data labeling effort. In this paper we describe the architecture and design of this system, and demonstrate its effectiveness through quantification of glioma brain tumors.
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Affiliation(s)
- Michael Nalisnik
- Department of Computer Science and Mathematics, Emory University, Emory University School of Medicine, Atlanta, GA 30322
| | - David A Gutman
- Department of Neurology, Emory University, Emory University School of Medicine, Atlanta, GA 30322
- Winship Cancer Institute, Emory University, Emory University School of Medicine, Atlanta, GA 30322
| | - Jun Kong
- Departments of Biomedical Informatics, Emory University School of Medicine/Georgia Institute of Technology, Atlanta, GA 30322
| | - Lee AD Cooper
- Departments of Biomedical Informatics, Emory University School of Medicine/Georgia Institute of Technology, Atlanta, GA 30322
- Department of Biomedical Engineering, Emory University School of Medicine/Georgia Institute of Technology, Atlanta, GA 30322
- Winship Cancer Institute, Emory University School of Medicine/Georgia Institute of Technology, Atlanta, GA 30322
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161
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Singh S, Wu X, Ljosa V, Bray MA, Piccioni F, Root DE, Doench JG, Boehm JS, Carpenter AE. Morphological Profiles of RNAi-Induced Gene Knockdown Are Highly Reproducible but Dominated by Seed Effects. PLoS One 2015. [PMID: 26197079 PMCID: PMC4511418 DOI: 10.1371/journal.pone.0131370] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
RNA interference and morphological profiling-the measurement of thousands of phenotypes from individual cells by microscopy and image analysis-are a potentially powerful combination. We show that morphological profiles of RNAi-induced knockdown using the Cell Painting assay are in fact highly sensitive and reproducible. However, we find that the magnitude and prevalence of off-target effects via the RNAi seed-based mechanism make morphological profiles of RNAi reagents targeting the same gene look no more similar than reagents targeting different genes. Pairs of RNAi reagents that share the same seed sequence produce image-based profiles that are much more similar to each other than profiles from pairs designed to target the same gene, a phenomenon previously observed in small-scale gene-expression profiling experiments. Various strategies have been used to enrich on-target versus off-target effects in the context of RNAi screening where a narrow set of phenotypes are measured, mostly based on comparing multiple sequences targeting the same gene; however, new approaches will be needed to make RNAi morphological profiling (that is, comparing multi-dimensional phenotypes) viable. We have shared our raw data and computational pipelines to facilitate research.
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Affiliation(s)
- Shantanu Singh
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Xiaoyun Wu
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Vebjorn Ljosa
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Mark-Anthony Bray
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Federica Piccioni
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - David E. Root
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - John G. Doench
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Jesse S. Boehm
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Anne E. Carpenter
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
- * E-mail:
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162
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Müller I, Hartmann D, Oertel J, Keck CM, Eichler H. Static image analysis as new approach for the characterization of tumor cell lysate used in dendritic cell vaccine preparation. Transfus Med Hemother 2015; 42:122-8. [PMID: 26019708 DOI: 10.1159/000371480] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2014] [Accepted: 07/31/2014] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Safety is an important consideration for the clinical application of dendritic cells (DC) loaded with autologous tumor lysate (TL). Thus, avitalization of TL from living autologous tumor tissue has to be guaranteed. METHODS Composition of TL was investigated by static image analysis (SIA) with the Morphologi G3 device, which simultaneously measures size and shape of up to 100,000 particles within one sample run. This approach was compared with sample characterization by high-resolution automated cell counting, trypan blue staining, and ATP quantification. RESULTS Using SIA, we only detected fragmented, non-cellular structures in completely avitalized TL, indicating complete destruction of living cells. Analysis of particle size distribution by SIA as well as CASY cell counter showed that 95% of particles had a diameter of <10 µm as a sign of cell fragmentation. Complete avitalization of TL was confirmed with trypan blue staining and ATP analysis. CONCLUSION Regarding generation of DC vaccines, the proof of avitality of TL from living tumor tissue can clearly be achieved by SIA alone or in combination with standard assays. Our data show that SIA is a highly precise method for TL characterization. The SIA device complies with FDA regulation and, therefore, might be suitable for characterization of cellular therapy medicinal products.
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Affiliation(s)
- Isabelle Müller
- Institute of Clinical Hemostaseology and Transfusion Medicine, Saarland University Medical Center, Homburg, Germany
| | - Dorothee Hartmann
- Institute of Clinical Hemostaseology and Transfusion Medicine, Saarland University Medical Center, Homburg, Germany
| | - Joachim Oertel
- Department of Neurosurgery, Saarland University Medical Center, Homburg, Germany
| | - Cornelia M Keck
- Applied Pharmacy, University of Applied Sciences Kaiserslautern, Pirmasens, Germany
| | - Hermann Eichler
- Institute of Clinical Hemostaseology and Transfusion Medicine, Saarland University Medical Center, Homburg, Germany
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163
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Wachsmuth M, Conrad C, Bulkescher J, Koch B, Mahen R, Isokane M, Pepperkok R, Ellenberg J. High-throughput fluorescence correlation spectroscopy enables analysis of proteome dynamics in living cells. Nat Biotechnol 2015; 33:384-9. [PMID: 25774713 DOI: 10.1038/nbt.3146] [Citation(s) in RCA: 109] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2014] [Accepted: 12/24/2014] [Indexed: 12/25/2022]
Abstract
To understand the function of cellular protein networks, spatial and temporal context is essential. Fluorescence correlation spectroscopy (FCS) is a single-molecule method to study the abundance, mobility and interactions of fluorescence-labeled biomolecules in living cells. However, manual acquisition and analysis procedures have restricted live-cell FCS to short-term experiments of a few proteins. Here, we present high-throughput (HT)-FCS, which automates screening and time-lapse acquisition of FCS data at specific subcellular locations and subsequent data analysis. We demonstrate its utility by studying the dynamics of 53 nuclear proteins. We made 60,000 measurements in 10,000 living human cells, to obtain biophysical parameters that allowed us to classify proteins according to their chromatin binding and complex formation. We also analyzed the cell-cycle-dependent dynamics of the mitotic kinase complex Aurora B/INCENP and showed how a rise in Aurora concentration triggers two-step complex formation. We expect that throughput and robustness will make HT-FCS a broadly applicable technology for characterizing protein network dynamics in cells.
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Affiliation(s)
- Malte Wachsmuth
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Christian Conrad
- 1] Advanced Light Microscopy Facility, European Molecular Biology Laboratory, Heidelberg, Germany. [2] Theoretical Bioinformatics, German Cancer Research Center/BioQuant, Heidelberg, Germany
| | - Jutta Bulkescher
- Advanced Light Microscopy Facility, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Birgit Koch
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Robert Mahen
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Mayumi Isokane
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Rainer Pepperkok
- 1] Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany. [2] Advanced Light Microscopy Facility, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Jan Ellenberg
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
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164
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Iannetti EF, Willems PHGM, Pellegrini M, Beyrath J, Smeitink JAM, Blanchet L, Koopman WJH. Toward high-content screening of mitochondrial morphology and membrane potential in living cells. Int J Biochem Cell Biol 2015; 63:66-70. [PMID: 25668473 DOI: 10.1016/j.biocel.2015.01.020] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2014] [Revised: 01/14/2015] [Accepted: 01/29/2015] [Indexed: 11/20/2022]
Abstract
Mitochondria are double membrane organelles involved in various key cellular processes. Governed by dedicated protein machinery, mitochondria move and continuously fuse and divide. These "mitochondrial dynamics" are bi-directionally linked to mitochondrial and cell functional state in space and time. Due to the action of the electron transport chain (ETC), the mitochondrial inner membrane displays a inside-negative membrane potential (Δψ). The latter is considered a functional readout of mitochondrial "health" and required to sustain normal mitochondrial ATP production and mitochondrial fusion. During the last decade, live-cell microscopy strategies were developed for simultaneous quantification of Δψ and mitochondrial morphology. This revealed that ETC dysfunction, changes in Δψ and aberrations in mitochondrial structure often occur in parallel, suggesting they are linked potential targets for therapeutic intervention. Here we discuss how combining high-content and high-throughput strategies can be used for analysis of genetic and/or drug-induced effects at the level of individual organelles, cells and cell populations. This article is part of a Directed Issue entitled: Energy Metabolism Disorders and Therapies.
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Affiliation(s)
| | - Peter H G M Willems
- Khondrion BV, Nijmegen, The Netherlands; Department of Biochemistry, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | | | - Jan A M Smeitink
- Khondrion BV, Nijmegen, The Netherlands; Department of Pediatrics, Nijmegen Center for Mitochondria disorders, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Werner J H Koopman
- Khondrion BV, Nijmegen, The Netherlands; Department of Biochemistry, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
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165
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Blanchet L, Smeitink JAM, van Emst-de Vries SE, Vogels C, Pellegrini M, Jonckheere AI, Rodenburg RJT, Buydens LMC, Beyrath J, Willems PHGM, Koopman WJH. Quantifying small molecule phenotypic effects using mitochondrial morpho-functional fingerprinting and machine learning. Sci Rep 2015; 5:8035. [PMID: 25620325 PMCID: PMC4306129 DOI: 10.1038/srep08035] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2014] [Accepted: 12/22/2014] [Indexed: 12/31/2022] Open
Abstract
In primary fibroblasts from Leigh Syndrome (LS) patients, isolated mitochondrial complex I deficiency is associated with increased reactive oxygen species levels and mitochondrial morpho-functional changes. Empirical evidence suggests these aberrations constitute linked therapeutic targets for small chemical molecules. However, the latter generally induce multiple subtle effects, meaning that in vitro potency analysis or single-parameter high-throughput cell screening are of limited use to identify these molecules. We combine automated image quantification and artificial intelligence to discriminate between primary fibroblasts of a healthy individual and a LS patient based upon their mitochondrial morpho-functional phenotype. We then evaluate the effects of newly developed Trolox variants in LS patient cells. This revealed that Trolox ornithylamide hydrochloride best counterbalanced mitochondrial morpho-functional aberrations, effectively scavenged ROS and increased the maximal activity of mitochondrial complexes I, IV and citrate synthase. Our results suggest that Trolox-derived antioxidants are promising candidates in therapy development for human mitochondrial disorders.
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Affiliation(s)
- Lionel Blanchet
- 1] Department of Biochemistry, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, P.O. Box 9101, NL-6500 HB Nijmegen, The Netherlands [2] Analytical Chemistry/Chemometrics, Institute for Molecules and Materials, Radboud University, postvak 61P.O. Box 9010, 6500 GL Nijmegen, The Netherlands [3] Centre for Systems Biology and Bioenergetics, Radboud University Medical Center, Nijmegen, The Netherlands [4] Khondrion BV, Philips van Leydenlaan 15, 6525EX Nijmegen, The Netherlands
| | - Jan A M Smeitink
- 1] Centre for Systems Biology and Bioenergetics, Radboud University Medical Center, Nijmegen, The Netherlands [2] Khondrion BV, Philips van Leydenlaan 15, 6525EX Nijmegen, The Netherlands [3] Department of Pediatrics, Nijmegen Center for Mitochondrial Disorders, Radboud University Medical Center, Nijmegen, Geert Grooteplein 10PO BOX 9101, 6500 HB Nijmegen, The Netherlands
| | - Sjenet E van Emst-de Vries
- 1] Department of Biochemistry, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, P.O. Box 9101, NL-6500 HB Nijmegen, The Netherlands [2] Khondrion BV, Philips van Leydenlaan 15, 6525EX Nijmegen, The Netherlands
| | - Caroline Vogels
- Khondrion BV, Philips van Leydenlaan 15, 6525EX Nijmegen, The Netherlands
| | - Mina Pellegrini
- Khondrion BV, Philips van Leydenlaan 15, 6525EX Nijmegen, The Netherlands
| | - An I Jonckheere
- Department of Pediatrics, Nijmegen Center for Mitochondrial Disorders, Radboud University Medical Center, Nijmegen, Geert Grooteplein 10PO BOX 9101, 6500 HB Nijmegen, The Netherlands
| | - Richard J T Rodenburg
- 1] Centre for Systems Biology and Bioenergetics, Radboud University Medical Center, Nijmegen, The Netherlands [2] Department of Pediatrics, Nijmegen Center for Mitochondrial Disorders, Radboud University Medical Center, Nijmegen, Geert Grooteplein 10PO BOX 9101, 6500 HB Nijmegen, The Netherlands
| | - Lutgarde M C Buydens
- 1] Analytical Chemistry/Chemometrics, Institute for Molecules and Materials, Radboud University, postvak 61P.O. Box 9010, 6500 GL Nijmegen, The Netherlands [2] Centre for Systems Biology and Bioenergetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Julien Beyrath
- Khondrion BV, Philips van Leydenlaan 15, 6525EX Nijmegen, The Netherlands
| | - Peter H G M Willems
- 1] Department of Biochemistry, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, P.O. Box 9101, NL-6500 HB Nijmegen, The Netherlands [2] Centre for Systems Biology and Bioenergetics, Radboud University Medical Center, Nijmegen, The Netherlands [3] Khondrion BV, Philips van Leydenlaan 15, 6525EX Nijmegen, The Netherlands
| | - Werner J H Koopman
- 1] Department of Biochemistry, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, P.O. Box 9101, NL-6500 HB Nijmegen, The Netherlands [2] Centre for Systems Biology and Bioenergetics, Radboud University Medical Center, Nijmegen, The Netherlands [3] Khondrion BV, Philips van Leydenlaan 15, 6525EX Nijmegen, The Netherlands
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166
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Higaki T, Kutsuna N, Akita K, Sato M, Sawaki F, Kobayashi M, Nagata N, Toyooka K, Hasezawa S. Semi-automatic organelle detection on transmission electron microscopic images. Sci Rep 2015; 5:7794. [PMID: 25589024 PMCID: PMC4295107 DOI: 10.1038/srep07794] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2014] [Accepted: 12/16/2014] [Indexed: 12/17/2022] Open
Abstract
Recent advances in the acquisition of large-scale datasets of transmission electron microscope images have allowed researchers to determine the number and the distribution of subcellular ultrastructures at both the cellular level and the tissue level. For this purpose, it would be very useful to have a computer-assisted system to detect the structures of interest, such as organelles. Using our original image recognition framework CARTA (Clustering-Aided Rapid Training Agent), combined with procedures to highlight and enlarge regions of interest on the image, we have developed a successful method for the semi-automatic detection of plant organelles including mitochondria, amyloplasts, chloroplasts, etioplasts, and Golgi stacks in transmission electron microscope images. Our proposed semi-automatic detection system will be helpful for labelling organelles in the interpretation and/or quantitative analysis of large-scale electron microscope imaging data.
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Affiliation(s)
- Takumi Higaki
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwanoha, Kashiwa 277-8562, Japan
| | - Natsumaro Kutsuna
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwanoha, Kashiwa 277-8562, Japan
- Research and Development Division, LPixel Inc., Bunkyo-ku, Tokyo 150-0002, Japan
| | - Kae Akita
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwanoha, Kashiwa 277-8562, Japan
| | - Mayuko Sato
- RIKEN Center for Sustainable Resource Science, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Fumie Sawaki
- Faculty of Science, Japan Women's University, Bunkyo-ku, Tokyo 112-8681, Japan
| | - Megumi Kobayashi
- Faculty of Science, Japan Women's University, Bunkyo-ku, Tokyo 112-8681, Japan
| | - Noriko Nagata
- Faculty of Science, Japan Women's University, Bunkyo-ku, Tokyo 112-8681, Japan
| | - Kiminori Toyooka
- RIKEN Center for Sustainable Resource Science, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Seiichiro Hasezawa
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwanoha, Kashiwa 277-8562, Japan
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167
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Graml V, Studera X, Lawson JLD, Chessel A, Geymonat M, Bortfeld-Miller M, Walter T, Wagstaff L, Piddini E, Carazo Salas RE. A genomic Multiprocess survey of machineries that control and link cell shape, microtubule organization, and cell-cycle progression. Dev Cell 2015; 31:227-239. [PMID: 25373780 DOI: 10.1016/j.devcel.2014.09.005] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2013] [Revised: 04/21/2014] [Accepted: 08/19/2014] [Indexed: 12/17/2022]
Abstract
Understanding cells as integrated systems requires that we systematically decipher how single genes affect multiple biological processes and how processes are functionally linked. Here, we used multiprocess phenotypic profiling, combining high-resolution 3D confocal microscopy and multiparametric image analysis, to simultaneously survey the fission yeast genome with respect to three key cellular processes: cell shape, microtubule organization, and cell-cycle progression. We identify, validate, and functionally annotate 262 genes controlling specific aspects of those processes. Of these, 62% had not been linked to these processes before and 35% are implicated in multiple processes. Importantly, we identify a conserved role for DNA-damage responses in controlling microtubule stability. In addition, we investigate how the processes are functionally linked. We show unexpectedly that disruption of cell-cycle progression does not necessarily affect cell size control and that distinct aspects of cell shape regulate microtubules and vice versa, identifying important systems-level links across these processes.
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Affiliation(s)
- Veronika Graml
- The Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, United Kingdom.,Genetics Department, University of Cambridge, Downing Street, Cambridge, CB2 3EH, United Kingdom.,Institute of Biochemistry, ETH Zurich, Schafmattstrasse 18, HPM G16.2, Zurich, CH-8093, Switzerland
| | - Xenia Studera
- The Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, United Kingdom.,Genetics Department, University of Cambridge, Downing Street, Cambridge, CB2 3EH, United Kingdom.,Institute of Biochemistry, ETH Zurich, Schafmattstrasse 18, HPM G16.2, Zurich, CH-8093, Switzerland
| | - Jonathan L D Lawson
- The Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, United Kingdom.,Genetics Department, University of Cambridge, Downing Street, Cambridge, CB2 3EH, United Kingdom
| | - Anatole Chessel
- The Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, United Kingdom.,Genetics Department, University of Cambridge, Downing Street, Cambridge, CB2 3EH, United Kingdom
| | - Marco Geymonat
- The Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, United Kingdom.,Genetics Department, University of Cambridge, Downing Street, Cambridge, CB2 3EH, United Kingdom
| | - Miriam Bortfeld-Miller
- Institute of Biochemistry, ETH Zurich, Schafmattstrasse 18, HPM G16.2, Zurich, CH-8093, Switzerland
| | - Thomas Walter
- Institut Curie, Centre for Computational Biology, Centre de Recherche Unité 900, 26 Rue d'Ulm, 75248 Paris, France
| | - Laura Wagstaff
- The Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, United Kingdom.,Zoology Department, University of Cambridge, Downing Street, Cambridge, CB2 3EJ, United Kingdom
| | - Eugenia Piddini
- The Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, United Kingdom.,Zoology Department, University of Cambridge, Downing Street, Cambridge, CB2 3EJ, United Kingdom
| | - Rafael E Carazo Salas
- The Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, United Kingdom.,Genetics Department, University of Cambridge, Downing Street, Cambridge, CB2 3EH, United Kingdom.,Institute of Biochemistry, ETH Zurich, Schafmattstrasse 18, HPM G16.2, Zurich, CH-8093, Switzerland
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168
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Herbert AD, Carr AM, Hoffmann E. FindFoci: a focus detection algorithm with automated parameter training that closely matches human assignments, reduces human inconsistencies and increases speed of analysis. PLoS One 2014; 9:e114749. [PMID: 25478967 PMCID: PMC4257716 DOI: 10.1371/journal.pone.0114749] [Citation(s) in RCA: 73] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2014] [Accepted: 11/13/2014] [Indexed: 11/19/2022] Open
Abstract
Accurate and reproducible quantification of the accumulation of proteins into foci in cells is essential for data interpretation and for biological inferences. To improve reproducibility, much emphasis has been placed on the preparation of samples, but less attention has been given to reporting and standardizing the quantification of foci. The current standard to quantitate foci in open-source software is to manually determine a range of parameters based on the outcome of one or a few representative images and then apply the parameter combination to the analysis of a larger dataset. Here, we demonstrate the power and utility of using machine learning to train a new algorithm (FindFoci) to determine optimal parameters. FindFoci closely matches human assignments and allows rapid automated exploration of parameter space. Thus, individuals can train the algorithm to mirror their own assignments and then automate focus counting using the same parameters across a large number of images. Using the training algorithm to match human assignments of foci, we demonstrate that applying an optimal parameter combination from a single image is not broadly applicable to analysis of other images scored by the same experimenter or by other experimenters. Our analysis thus reveals wide variation in human assignment of foci and their quantification. To overcome this, we developed training on multiple images, which reduces the inconsistency of using a single or a few images to set parameters for focus detection. FindFoci is provided as an open-source plugin for ImageJ.
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Affiliation(s)
- Alex D. Herbert
- MRC Genome Damage and Stability Centre, School of Life Sciences, University of Sussex, Brighton, BN1 9RQ, United Kingdom
- * E-mail:
| | - Antony M. Carr
- MRC Genome Damage and Stability Centre, School of Life Sciences, University of Sussex, Brighton, BN1 9RQ, United Kingdom
| | - Eva Hoffmann
- MRC Genome Damage and Stability Centre, School of Life Sciences, University of Sussex, Brighton, BN1 9RQ, United Kingdom
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169
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Katsonis P, Koire A, Wilson SJ, Hsu TK, Lua RC, Wilkins AD, Lichtarge O. Single nucleotide variations: biological impact and theoretical interpretation. Protein Sci 2014; 23:1650-66. [PMID: 25234433 PMCID: PMC4253807 DOI: 10.1002/pro.2552] [Citation(s) in RCA: 78] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2014] [Revised: 09/12/2014] [Accepted: 09/15/2014] [Indexed: 12/27/2022]
Abstract
Genome-wide association studies (GWAS) and whole-exome sequencing (WES) generate massive amounts of genomic variant information, and a major challenge is to identify which variations drive disease or contribute to phenotypic traits. Because the majority of known disease-causing mutations are exonic non-synonymous single nucleotide variations (nsSNVs), most studies focus on whether these nsSNVs affect protein function. Computational studies show that the impact of nsSNVs on protein function reflects sequence homology and structural information and predict the impact through statistical methods, machine learning techniques, or models of protein evolution. Here, we review impact prediction methods and discuss their underlying principles, their advantages and limitations, and how they compare to and complement one another. Finally, we present current applications and future directions for these methods in biological research and medical genetics.
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Affiliation(s)
- Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of MedicineHouston, Texas
| | - Amanda Koire
- Department of Structural and Computational Biology and Molecular BiophysicsHouston, Texas
| | - Stephen Joseph Wilson
- Department of Biochemistry and Molecular Biology, Baylor College of MedicineHouston, Texas
| | - Teng-Kuei Hsu
- Department of Biochemistry and Molecular Biology, Baylor College of MedicineHouston, Texas
| | - Rhonald C Lua
- Department of Molecular and Human Genetics, Baylor College of MedicineHouston, Texas
| | - Angela Dawn Wilkins
- Department of Molecular and Human Genetics, Baylor College of MedicineHouston, Texas
- Computational and Integrative Biomedical Research Center, Baylor College of MedicineHouston, Texas
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of MedicineHouston, Texas
- Department of Structural and Computational Biology and Molecular BiophysicsHouston, Texas
- Department of Biochemistry and Molecular Biology, Baylor College of MedicineHouston, Texas
- Computational and Integrative Biomedical Research Center, Baylor College of MedicineHouston, Texas
- Department of Pharmacology, Baylor College of MedicineHouston, Texas
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