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Zhu XL, Bao LX, Xue MQ, Xu YY. Automatic recognition of protein subcellular location patterns in single cells from immunofluorescence images based on deep learning. Brief Bioinform 2023; 24:6964519. [PMID: 36577448 DOI: 10.1093/bib/bbac609] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 11/16/2022] [Accepted: 12/11/2022] [Indexed: 12/30/2022] Open
Abstract
With the improvement of single-cell measurement techniques, there is a growing awareness that individual differences exist among cells, and protein expression distribution can vary across cells in the same tissue or cell line. Pinpointing the protein subcellular locations in single cells is crucial for mapping functional specificity of proteins and studying related diseases. Currently, research about single-cell protein location is still in its infancy, and most studies and databases do not annotate proteins at the cell level. For example, in the human protein atlas database, an immunofluorescence image stained for a particular protein shows multiple cells, but the subcellular location annotation is for the whole image, ignoring intercellular difference. In this study, we used large-scale immunofluorescence images and image-level subcellular locations to develop a deep-learning-based pipeline that could accurately recognize protein localizations in single cells. The pipeline consisted of two deep learning models, i.e. an image-based model and a cell-based model. The former used a multi-instance learning framework to comprehensively model protein distribution in multiple cells in each image, and could give both image-level and cell-level predictions. The latter firstly used clustering and heuristics algorithms to assign pseudo-labels of subcellular locations to the segmented cell images, and then used the pseudo-labels to train a classification model. Finally, the image-based model was fused with the cell-based model at the decision level to obtain the final ensemble model for single-cell prediction. Our experimental results showed that the ensemble model could achieve higher accuracy and robustness on independent test sets than state-of-the-art methods.
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Affiliation(s)
- Xi-Liang Zhu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China
| | - Lin-Xia Bao
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China
| | - Min-Qi Xue
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China
| | - Ying-Ying Xu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China
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2
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Xue MQ, Zhu XL, Wang G, Xu YY. DULoc: quantitatively unmixing protein subcellular location patterns in immunofluorescence images based on deep learning features. Bioinformatics 2022; 38:827-833. [PMID: 34694372 DOI: 10.1093/bioinformatics/btab730] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 09/13/2021] [Accepted: 10/20/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Knowledge of subcellular locations of proteins is of great significance for understanding their functions. The multi-label proteins that simultaneously reside in or move between more than one subcellular structure usually involve with complex cellular processes. Currently, the subcellular location annotations of proteins in most studies and databases are descriptive terms, which fail to capture the protein amount or fractions across different locations. This highly limits the understanding of complex spatial distribution and functional mechanism of multi-label proteins. Thus, quantitatively analyzing the multiplex location patterns of proteins is an urgent and challenging task. RESULTS In this study, we developed a deep-learning-based pattern unmixing pipeline for protein subcellular localization (DULoc) to quantitatively estimate the fractions of proteins localizing in different subcellular compartments from immunofluorescence images. This model used a deep convolutional neural network to construct feature representations, and combined multiple nonlinear decomposing algorithms as the pattern unmixing method. Our experimental results showed that the DULoc can achieve over 0.93 correlation between estimated and true fractions on both real and synthetic datasets. In addition, we applied the DULoc method on the images in the human protein atlas database on a large scale, and showed that 70.52% of proteins can achieve consistent location orders with the database annotations. AVAILABILITY AND IMPLEMENTATION The datasets and code are available at: https://github.com/PRBioimages/DULoc. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Min-Qi Xue
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China
| | - Xi-Liang Zhu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China
| | - Ge Wang
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China
| | - Ying-Ying Xu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China
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3
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Deep learning is combined with massive-scale citizen science to improve large-scale image classification. Nat Biotechnol 2018; 36:820-828. [PMID: 30125267 DOI: 10.1038/nbt.4225] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2017] [Accepted: 07/19/2018] [Indexed: 01/11/2023]
Abstract
Pattern recognition and classification of images are key challenges throughout the life sciences. We combined two approaches for large-scale classification of fluorescence microscopy images. First, using the publicly available data set from the Cell Atlas of the Human Protein Atlas (HPA), we integrated an image-classification task into a mainstream video game (EVE Online) as a mini-game, named Project Discovery. Participation by 322,006 gamers over 1 year provided nearly 33 million classifications of subcellular localization patterns, including patterns that were not previously annotated by the HPA. Second, we used deep learning to build an automated Localization Cellular Annotation Tool (Loc-CAT). This tool classifies proteins into 29 subcellular localization patterns and can deal efficiently with multi-localization proteins, performing robustly across different cell types. Combining the annotations of gamers and deep learning, we applied transfer learning to create a boosted learner that can characterize subcellular protein distribution with F1 score of 0.72. We found that engaging players of commercial computer games provided data that augmented deep learning and enabled scalable and readily improved image classification.
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Gretzmeier C, Eiselein S, Johnson GR, Engelke R, Nowag H, Zarei M, Küttner V, Becker AC, Rigbolt KTG, Høyer-Hansen M, Andersen JS, Münz C, Murphy RF, Dengjel J. Degradation of protein translation machinery by amino acid starvation-induced macroautophagy. Autophagy 2017; 13:1064-1075. [PMID: 28453381 DOI: 10.1080/15548627.2016.1274485] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Macroautophagy is regarded as a nonspecific bulk degradation process of cytoplasmic material within the lysosome. However, the process has mainly been studied by nonspecific bulk degradation assays using radiolabeling. In the present study we monitor protein turnover and degradation by global, unbiased approaches relying on quantitative mass spectrometry-based proteomics. Macroautophagy is induced by rapamycin treatment, and by amino acid and glucose starvation in differentially, metabolically labeled cells. Protein dynamics are linked to image-based models of autophagosome turnover. Depending on the inducing stimulus, protein as well as organelle turnover differ. Amino acid starvation-induced macroautophagy leads to selective degradation of proteins important for protein translation. Thus, protein dynamics reflect cellular conditions in the respective treatment indicating stimulus-specific pathways in stress-induced macroautophagy.
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Affiliation(s)
- Christine Gretzmeier
- a Department of Dermatology , Medical Center - University of Freiburg , Freiburg , Germany.,b Freiburg Institute for Advanced Studies (FRIAS), and ZBSA Center for Biological Systems Analysis, University of Freiburg , Freiburg , Germany
| | - Sven Eiselein
- a Department of Dermatology , Medical Center - University of Freiburg , Freiburg , Germany.,b Freiburg Institute for Advanced Studies (FRIAS), and ZBSA Center for Biological Systems Analysis, University of Freiburg , Freiburg , Germany
| | - Gregory R Johnson
- c Computational Biology Department , Carnegie Mellon University , Pittsburgh , PA , USA
| | - Rudolf Engelke
- b Freiburg Institute for Advanced Studies (FRIAS), and ZBSA Center for Biological Systems Analysis, University of Freiburg , Freiburg , Germany
| | - Heike Nowag
- d Institute of Experimental Immunology, University of Zürich , Zürich , Switzerland
| | - Mostafa Zarei
- a Department of Dermatology , Medical Center - University of Freiburg , Freiburg , Germany.,b Freiburg Institute for Advanced Studies (FRIAS), and ZBSA Center for Biological Systems Analysis, University of Freiburg , Freiburg , Germany
| | - Victoria Küttner
- a Department of Dermatology , Medical Center - University of Freiburg , Freiburg , Germany.,b Freiburg Institute for Advanced Studies (FRIAS), and ZBSA Center for Biological Systems Analysis, University of Freiburg , Freiburg , Germany
| | - Andrea C Becker
- b Freiburg Institute for Advanced Studies (FRIAS), and ZBSA Center for Biological Systems Analysis, University of Freiburg , Freiburg , Germany
| | - Kristoffer T G Rigbolt
- b Freiburg Institute for Advanced Studies (FRIAS), and ZBSA Center for Biological Systems Analysis, University of Freiburg , Freiburg , Germany
| | - Maria Høyer-Hansen
- e Apoptosis Department and Center for Genotoxic Stress Research , Danish Cancer Society , Copenhagen , Denmark
| | - Jens S Andersen
- f Center for Experimental BioInformatics , Department of Biochemistry and Molecular Biology, University of Southern Denmark , Odense , Denmark
| | - Christian Münz
- d Institute of Experimental Immunology, University of Zürich , Zürich , Switzerland
| | - Robert F Murphy
- b Freiburg Institute for Advanced Studies (FRIAS), and ZBSA Center for Biological Systems Analysis, University of Freiburg , Freiburg , Germany.,c Computational Biology Department , Carnegie Mellon University , Pittsburgh , PA , USA
| | - Jörn Dengjel
- a Department of Dermatology , Medical Center - University of Freiburg , Freiburg , Germany.,b Freiburg Institute for Advanced Studies (FRIAS), and ZBSA Center for Biological Systems Analysis, University of Freiburg , Freiburg , Germany.,g Department of Biology , University of Fribourg , Fribourg , Switzerland
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5
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Yang Q, Zou HY, Zhang Y, Tang LJ, Shen GL, Jiang JH, Yu RQ. Multiplex protein pattern unmixing using a non-linear variable-weighted support vector machine as optimized by a particle swarm optimization algorithm. Talanta 2016; 147:609-14. [DOI: 10.1016/j.talanta.2015.10.047] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Revised: 10/14/2015] [Accepted: 10/18/2015] [Indexed: 11/30/2022]
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6
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Coelho LP, Kangas JD, Naik AW, Osuna-Highley E, Glory-Afshar E, Fuhrman M, Simha R, Berget PB, Jarvik JW, Murphy RF. Determining the subcellular location of new proteins from microscope images using local features. ACTA ACUST UNITED AC 2013; 29:2343-9. [PMID: 23836142 DOI: 10.1093/bioinformatics/btt392] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION Evaluation of previous systems for automated determination of subcellular location from microscope images has been done using datasets in which each location class consisted of multiple images of the same representative protein. Here, we frame a more challenging and useful problem where previously unseen proteins are to be classified. RESULTS Using CD-tagging, we generated two new image datasets for evaluation of this problem, which contain several different proteins for each location class. Evaluation of previous methods on these new datasets showed that it is much harder to train a classifier that generalizes across different proteins than one that simply recognizes a protein it was trained on. We therefore developed and evaluated additional approaches, incorporating novel modifications of local features techniques. These extended the notion of local features to exploit both the protein image and any reference markers that were imaged in parallel. With these, we obtained a large accuracy improvement in our new datasets over existing methods. Additionally, these features help achieve classification improvements for other previously studied datasets. AVAILABILITY The datasets are available for download at http://murphylab.web.cmu.edu/data/. The software was written in Python and C++ and is available under an open-source license at http://murphylab.web.cmu.edu/software/. The code is split into a library, which can be easily reused for other data and a small driver script for reproducing all results presented here. A step-by-step tutorial on applying the methods to new datasets is also available at that address. CONTACT murphy@cmu.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Luis Pedro Coelho
- Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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7
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Handfield LF, Chong YT, Simmons J, Andrews BJ, Moses AM. Unsupervised clustering of subcellular protein expression patterns in high-throughput microscopy images reveals protein complexes and functional relationships between proteins. PLoS Comput Biol 2013; 9:e1003085. [PMID: 23785265 PMCID: PMC3681667 DOI: 10.1371/journal.pcbi.1003085] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2012] [Accepted: 04/19/2013] [Indexed: 12/11/2022] Open
Abstract
Protein subcellular localization has been systematically characterized in budding yeast using fluorescently tagged proteins. Based on the fluorescence microscopy images, subcellular localization of many proteins can be classified automatically using supervised machine learning approaches that have been trained to recognize predefined image classes based on statistical features. Here, we present an unsupervised analysis of protein expression patterns in a set of high-resolution, high-throughput microscope images. Our analysis is based on 7 biologically interpretable features which are evaluated on automatically identified cells, and whose cell-stage dependency is captured by a continuous model for cell growth. We show that it is possible to identify most previously identified localization patterns in a cluster analysis based on these features and that similarities between the inferred expression patterns contain more information about protein function than can be explained by a previous manual categorization of subcellular localization. Furthermore, the inferred cell-stage associated to each fluorescence measurement allows us to visualize large groups of proteins entering the bud at specific stages of bud growth. These correspond to proteins localized to organelles, revealing that the organelles must be entering the bud in a stereotypical order. We also identify and organize a smaller group of proteins that show subtle differences in the way they move around the bud during growth. Our results suggest that biologically interpretable features based on explicit models of cell morphology will yield unprecedented power for pattern discovery in high-resolution, high-throughput microscopy images. The location of a particular protein in the cell is one of the most important pieces of information that cell biologists use to understand its function. Fluorescent tags are a powerful way to determine the location of a protein in living cells. Nearly a decade ago, a collection of yeast strains was introduced, where in each strain a single protein was tagged with green fluorescent protein (GFP). Here, we show that by training a computer to accurately identify the buds of growing yeast cells, and then making simple fluorescence measurements in context of cell shape and cell stage, the computer could automatically discover most of the localization patterns (nucleus, cytoplasm, mitochondria, etc.) without any prior knowledge of what the patterns might be. Because we made the same, simple measurements for each yeast cell, we could compare and visualize the patterns of fluorescence for the entire collection of strains. This allowed us to identify large groups of proteins moving around the cell in a coordinated fashion, and to identify new, complex patterns that had previously been difficult to describe.
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Affiliation(s)
| | - Yolanda T. Chong
- Department of Molecular Genetics, University of Toronto, Ontario, Canada
| | - Jibril Simmons
- Department of Cell & Systems Biology, University of Toronto, Ontario, Canada
| | - Brenda J. Andrews
- Department of Molecular Genetics, University of Toronto, Ontario, Canada
| | - Alan M. Moses
- Department of Computer Science, University of Toronto, Ontario, Canada
- Department of Cell & Systems Biology, University of Toronto, Ontario, Canada
- * E-mail:
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8
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Xu YY, Yang F, Zhang Y, Shen HB. An image-based multi-label human protein subcellular localization predictor (iLocator) reveals protein mislocalizations in cancer tissues. ACTA ACUST UNITED AC 2013; 29:2032-40. [PMID: 23740749 DOI: 10.1093/bioinformatics/btt320] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
MOTIVATION Human cells are organized into compartments of different biochemical cellular processes. Having proteins appear at the right time to the correct locations in the cellular compartments is required to conduct their functions in normal cells, whereas mislocalization of proteins can result in pathological diseases, including cancer. RESULTS To reveal the cancer-related protein mislocalizations, we developed an image-based multi-label subcellular location predictor, iLocator, which covers seven cellular localizations. The iLocator incorporates both global and local image descriptors and generates predictions by using an ensemble multi-label classifier. The algorithm has the ability to treat both single- and multiple-location proteins. We first trained and tested iLocator on 3240 normal human tissue images that have known subcellular location information from the human protein atlas. The iLocator was then used to generate protein localization predictions for 3696 protein images from seven cancer tissues that have no location annotations in the human protein atlas. By comparing the output data from normal and cancer tissues, we detected eight potential cancer biomarker proteins that have significant localization differences with P-value < 0.01. AVAILABILITY http://www.csbio.sjtu.edu.cn/bioinf/iLocator/
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Affiliation(s)
- Ying-Ying Xu
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China
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9
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Antony PMA, Trefois C, Stojanovic A, Baumuratov AS, Kozak K. Light microscopy applications in systems biology: opportunities and challenges. Cell Commun Signal 2013; 11:24. [PMID: 23578051 PMCID: PMC3627909 DOI: 10.1186/1478-811x-11-24] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2013] [Accepted: 03/28/2013] [Indexed: 01/05/2023] Open
Abstract
Biological systems present multiple scales of complexity, ranging from molecules to entire populations. Light microscopy is one of the least invasive techniques used to access information from various biological scales in living cells. The combination of molecular biology and imaging provides a bottom-up tool for direct insight into how molecular processes work on a cellular scale. However, imaging can also be used as a top-down approach to study the behavior of a system without detailed prior knowledge about its underlying molecular mechanisms. In this review, we highlight the recent developments on microscopy-based systems analyses and discuss the complementary opportunities and different challenges with high-content screening and high-throughput imaging. Furthermore, we provide a comprehensive overview of the available platforms that can be used for image analysis, which enable community-driven efforts in the development of image-based systems biology.
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Affiliation(s)
- Paul Michel Aloyse Antony
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Christophe Trefois
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Aleksandar Stojanovic
- Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg City, Luxembourg
| | | | - Karol Kozak
- Light Microscopy Centre (LMSC), Institute for Biochemistry, ETH Zurich, Zurich, Switzerland
- Medical Faculty, Technical University Dresden, Dresden, Germany
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10
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Li YX, Ji S, Kumar S, Ye J, Zhou ZH. Drosophila gene expression pattern annotation through multi-instance multi-label learning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2012; 9:98-112. [PMID: 21519115 DOI: 10.1109/tcbb.2011.73] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
In the studies of Drosophila embryogenesis, a large number of two-dimensional digital images of gene expression patterns have been produced to build an atlas of spatio-temporal gene expression dynamics across developmental time. Gene expressions captured in these images have been manually annotated with anatomical and developmental ontology terms using a controlled vocabulary (CV), which are useful in research aimed at understanding gene functions, interactions, and networks. With the rapid accumulation of images, the process of manual annotation has become increasingly cumbersome, and computational methods to automate this task are urgently needed. However, the automated annotation of embryo images is challenging. This is because the annotation terms spatially correspond to local expression patterns of images, yet they are assigned collectively to groups of images and it is unknown which term corresponds to which region of which image in the group. In this paper, we address this problem using a new machine learning framework, Multi-Instance Multi-Label (MIML) learning. We first show that the underlying nature of the annotation task is a typical MIML learning problem. Then, we propose two support vector machine algorithms under the MIML framework for the task. Experimental results on the FlyExpress database (a digital library of standardized Drosophila gene expression pattern images) reveal that the exploitation of MIML framework leads to significant performance improvement over state-of-the-art approaches.
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Murphy RF. CellOrganizer: Image-derived models of subcellular organization and protein distribution. Methods Cell Biol 2012; 110:179-93. [PMID: 22482949 DOI: 10.1016/b978-0-12-388403-9.00007-2] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
This chapter describes approaches for learning models of subcellular organization from images. The primary utility of these models is expected to be from incorporation into complex simulations of cell behaviors. Most current cell simulations do not consider spatial organization of proteins at all, or treat each organelle type as a single, idealized compartment. The ability to build generative models for all proteins in a proteome and use them for spatially accurate simulations is expected to improve the accuracy of models of cell behaviors. A second use, of potentially equal importance, is expected to be in testing and comparing software for analyzing cell images. The complexity and sophistication of algorithms used in cell-image-based screens and assays (variously referred to as high-content screening, high-content analysis, or high-throughput microscopy) is continuously increasing, and generative models can be used to produce images for testing these algorithms in which the expected answer is known.
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Affiliation(s)
- Robert F Murphy
- Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
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12
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Jackson C, Glory-Afshar E, Murphy RF, Kovacevic J. Model building and intelligent acquisition with application to protein subcellular location classification. Bioinformatics 2011; 27:1854-9. [PMID: 21558154 DOI: 10.1093/bioinformatics/btr286] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION We present a framework and algorithms to intelligently acquire movies of protein subcellular location patterns by learning their models as they are being acquired, and simultaneously determining how many cells to acquire as well as how many frames to acquire per cell. This is motivated by the desire to minimize acquisition time and photobleaching, given the need to build such models for all proteins, in all cell types, under all conditions. Our key innovation is to build models during acquisition rather than as a post-processing step, thus allowing us to intelligently and automatically adapt the acquisition process given the model acquired. RESULTS We validate our framework on protein subcellular location classification, and show that the combination of model building and intelligent acquisition results in time and storage savings without loss of classification accuracy, or alternatively, higher classification accuracy for the same total acquisition time. AVAILABILITY AND IMPLEMENTATION The data and software used for this study will be made available upon publication at http://murphylab.web.cmu.edu/software and http://www.andrew.cmu.edu/user/jelenak/Software. CONTACT jelenak@cmu.edu.
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Affiliation(s)
- C Jackson
- Center for Bioimage Informatics, Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA
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Peng T, Murphy RF. Image-derived, three-dimensional generative models of cellular organization. Cytometry A 2011; 79:383-91. [PMID: 21472848 DOI: 10.1002/cyto.a.21066] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2010] [Revised: 03/04/2011] [Accepted: 03/14/2011] [Indexed: 02/01/2023]
Abstract
Given the importance of subcellular location to protein function, computational simulations of cell behaviors will ultimately require the ability to model the distributions of proteins within organelles and other structures. Toward this end, statistical learning methods have previously been used to build models of sets of two-dimensional microscope images, where each set contains multiple images for a single subcellular location pattern. The model learned from each set of images not only represents the pattern but also captures the variation in that pattern from cell to cell. The models consist of sub-models for nuclear shape, cell shape, organelle size and shape, and organelle distribution relative to nuclear and cell boundaries, and allow synthesis of images with the expectation that they are drawn from the same underlying statistical distribution as the images used to train them. Here we extend this generative models approach to three dimensions using a similar framework, permitting protein subcellular locations to be described more accurately. Models of different patterns can be combined to yield a synthetic multi-channel image containing as many proteins as desired, something that is difficult to obtain by direct microscope imaging for more than a few proteins. In addition, the model parameters represent a more compact and interpretable way of communicating subcellular patterns than descriptive image features and may be particularly effective for automated identification of changes in subcellular organization caused by perturbagens.
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Affiliation(s)
- Tao Peng
- Center for Bioimage Informatics, and Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, Pennsylvania 15213, USA
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Woolfe F, Gerdes M, Bello M, Tao X, Can A. Autofluorescence removal by non-negative matrix factorization. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2011; 20:1085-1093. [PMID: 20889433 DOI: 10.1109/tip.2010.2079810] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
This paper describes a new, physically interpretable, fully automatic algorithm for removal of tissue autofluorescence (AF) from fluorescence microscopy images, by non-negative matrix factorization. Measurement of signal intensities from the concentration of certain fluorescent reporter molecules at each location within a sample of biological tissue is confounded by fluorescence produced by the tissue itself (autofluorescence). Spectral mixing models use mixing coefficients to specify how much fluorescence from each source is present and unmixing algorithms separate the two fluorescent sources. Current spectral unmixing methods for AF removal often require a priori knowledge of mixing coefficients. Those which do not, such as principal component analysis, generate negative mixing coefficients that are not physically meaningful. Non-negative matrix factorization constrains mixing coefficients to be non-negative, and has been used for spectral unmixing, but not AF removal. This paper describes a novel non-negative matrix factorization algorithm which separates fluorescent images into true signal and AF components utilizing an estimate of the dark current. We also present a test-bed, based on fluorescent beads, to compare the performance of different AF removal algorithms. Our algorithm out-performed previous state of the art on validation images.
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Affiliation(s)
- Franco Woolfe
- Yale University Applied Math, New Haven, CT 06511, USA.
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15
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Coelho LP, Peng T, Murphy RF. Quantifying the distribution of probes between subcellular locations using unsupervised pattern unmixing. Bioinformatics 2010; 26:i7-12. [PMID: 20529939 PMCID: PMC2881404 DOI: 10.1093/bioinformatics/btq220] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Motivation: Proteins exhibit complex subcellular distributions, which may include localizing in more than one organelle and varying in location depending on the cell physiology. Estimating the amount of protein distributed in each subcellular location is essential for quantitative understanding and modeling of protein dynamics and how they affect cell behaviors. We have previously described automated methods using fluorescent microscope images to determine the fractions of protein fluorescence in various subcellular locations when the basic locations in which a protein can be present are known. As this set of basic locations may be unknown (especially for studies on a proteome-wide scale), we here describe unsupervised methods to identify the fundamental patterns from images of mixed patterns and estimate the fractional composition of them. Methods: We developed two approaches to the problem, both based on identifying types of objects present in images and representing patterns by frequencies of those object types. One is a basis pursuit method (which is based on a linear mixture model), and the other is based on latent Dirichlet allocation (LDA). For testing both approaches, we used images previously acquired for testing supervised unmixing methods. These images were of cells labeled with various combinations of two organelle-specific probes that had the same fluorescent properties to simulate mixed patterns of subcellular location. Results: We achieved 0.80 and 0.91 correlation between estimated and underlying fractions of the two probes (fundamental patterns) with basis pursuit and LDA approaches, respectively, indicating that our methods can unmix the complex subcellular distribution with reasonably high accuracy. Availability:http://murphylab.web.cmu.edu/software Contact:murphy@cmu.edu
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Affiliation(s)
- Luis Pedro Coelho
- Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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16
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Abstract
To build more accurate models of cells and tissues, the ability to incorporate information on the distributions of proteins (and other macromolecules) will become increasingly important. This review describes current progress towards determining and representing protein subcellular patterns so that the information can be used as part of systems biology efforts. Approaches to decomposing an image of the subcellular pattern of a protein give critical information about the fraction of that protein in each of a number of fundamental patterns (e.g., organelles). Methods for learning generative models from images provide a means of capturing the essential properties and variation in those properties of cell shape and organelle patterns. The combination of models of fundamental patterns and vectors specifying the fraction of a protein in each of them provide a much better means of communicating subcellular patterns than the descriptive terms that are currently used. Communicating information about subcellular patterns is important not only for systems biology simulations but also for representing results from microscopy experiments, including high content screening and imaging flow cytometry, in a transportable and generalizable manner.
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Affiliation(s)
- Robert F Murphy
- Lane Center for Computational Biology and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA.
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Determining the distribution of probes between different subcellular locations through automated unmixing of subcellular patterns. Proc Natl Acad Sci U S A 2010; 107:2944-9. [PMID: 20133616 DOI: 10.1073/pnas.0912090107] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Many proteins or other biological macromolecules are localized to more than one subcellular structure. The fraction of a protein in different cellular compartments is often measured by colocalization with organelle-specific fluorescent markers, requiring availability of fluorescent probes for each compartment and acquisition of images for each in conjunction with the macromolecule of interest. Alternatively, tailored algorithms allow finding particular regions in images and quantifying the amount of fluorescence they contain. Unfortunately, this approach requires extensive hand-tuning of algorithms and is often cell type-dependent. Here we describe a machine-learning approach for estimating the amount of fluorescent signal in different subcellular compartments without hand tuning, requiring only the acquisition of separate training images of markers for each compartment. In testing on images of cells stained with mixtures of probes for different organelles, we achieved a 93% correlation between estimated and expected amounts of probes in each compartment. We also demonstrated that the method can be used to quantify drug-dependent protein translocations. The method enables automated and unbiased determination of the distributions of protein across cellular compartments, and will significantly improve imaging-based high-throughput assays and facilitate proteome-scale localization efforts.
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Long X, Cleveland WL, Yao YL. Multiclass detection of cells in multicontrast composite images. Comput Biol Med 2010; 40:168-78. [PMID: 20022596 PMCID: PMC2870534 DOI: 10.1016/j.compbiomed.2009.11.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2009] [Revised: 10/14/2009] [Accepted: 11/24/2009] [Indexed: 10/20/2022]
Abstract
In this paper, we describe a framework for multiclass cell detection in composite images consisting of images obtained with three different contrast methods for transmitted light illumination (referred to as multicontrast composite images). Compared to previous multiclass cell detection results [1], the use of multicontrast composite images was found to improve the detection accuracy by introducing more discriminatory information into the system. Preprocessing multicontrast composite images with Kernel PCA was found to be superior to traditional linear PCA preprocessing, especially in difficult classification scenarios where high-order nonlinear correlations are expected to be important. Systematic study of our approach under different overlap conditions suggests that it possesses sufficient speed and accuracy for use in some practical systems.
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Affiliation(s)
- Xi Long
- Mechanical Engineering Department, Columbia University, New York, NY 10027, USA
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20
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Newberg J, Hua J, Murphy RF. Location proteomics: systematic determination of protein subcellular location. Methods Mol Biol 2009; 500:313-332. [PMID: 19399439 DOI: 10.1007/978-1-59745-525-1_11] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Proteomics seeks the systematic and comprehensive understanding of all aspects of proteins, and location proteomics is the relatively new subfield of proteomics concerned with the location of proteins within cells. This review provides a guide to the widening selection of methods for studying location proteomics and integrating the results into systems biology. Automated and objective methods for determining protein subcellular location have been described based on extracting numerical features from fluorescence microscope images and applying machine learning approaches to them. Systems to recognize all major protein subcellular location patterns in both two-dimensional and three-dimensional HeLa cell images with high accuracy (over 95% and 98%, respectively) have been built. The feasibility of objectively grouping proteins into subcellular location families, and in the process of discovering new subcellular patterns, has been demonstrated using cluster analysis of images from a library of randomly tagged protein clones. Generative models can be built to effectively capture and communicate the patterns in these families. While automated methods for high-resolution determination of subcellular location are now available, the task of applying these methods to all expressed proteins in many different cell types under many conditions represents a very significant challenge.
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Affiliation(s)
- Justin Newberg
- Department of Biomedical Engineering and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburg, PA, USA
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21
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22
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McCullough DP, Gudla PR, Harris BS, Collins JA, Meaburn KJ, Nakaya MA, Yamaguchi TP, Misteli T, Lockett SJ. Segmentation of whole cells and cell nuclei from 3-D optical microscope images using dynamic programming. IEEE TRANSACTIONS ON MEDICAL IMAGING 2008; 27:723-34. [PMID: 18450544 PMCID: PMC2730109 DOI: 10.1109/tmi.2007.913135] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Communications between cells in large part drive tissue development and function, as well as disease-related processes such as tumorigenesis. Understanding the mechanistic bases of these processes necessitates quantifying specific molecules in adjacent cells or cell nuclei of intact tissue. However, a major restriction on such analyses is the lack of an efficient method that correctly segments each object (cell or nucleus) from 3-D images of an intact tissue specimen. We report a highly reliable and accurate semi-automatic algorithmic method for segmenting fluorescence-labeled cells or nuclei from 3-D tissue images. Segmentation begins with semi-automatic, 2-D object delineation in a user-selected plane, using dynamic programming (DP) to locate the border with an accumulated intensity per unit length greater that any other possible border around the same object. Then the two surfaces of the object in planes above and below the selected plane are found using an algorithm that combines DP and combinatorial searching. Following segmentation, any perceived errors can be interactively corrected. Segmentation accuracy is not significantly affected by intermittent labeling of object surfaces, diffuse surfaces, or spurious signals away from surfaces. The unique strength of the segmentation method was demonstrated on a variety of biological tissue samples where all cells, including irregularly shaped cells, were accurately segmented based on visual inspection.
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Affiliation(s)
| | - Prabhakar R. Gudla
- Image Analysis Laboratory, Advanced Technology Program, SAIC—Frederick, National Cancer Institute, Frederick, MD 21702 USA (e-mail: )
| | - Bradley S. Harris
- Image Analysis Laboratory, Advanced Technology Program, SAIC—Frederick, National Cancer Institute, Frederick, MD 21702 USA. He is now with Carl Zeiss, Inc., Thornwood, NY 10594 USA (e-mail: )
| | - Jason A. Collins
- Image Analysis Laboratory, Advanced Technology Program, SAIC—Frederick, National Cancer Institute, Frederick, MD 21702 USA (e-mail: )
| | - Karen J. Meaburn
- Cell Biology of Genomes Group, National Cancer Institute, Bethesda, MD 20892 USA (e-mail: meaburnk@mail. nih.gov)
| | - Masa-Aki Nakaya
- Cancer and Developmental Biology Laboratory, National Cancer Institute, Frederick, MD 21702 USA. He is now with the Department of Histology and Embryology, Graduate School of Medical Science, Kanazawa University 13-1, Takara-machi, Kanazawa 920-8640, Japan (e-mail: )
| | - Terry P. Yamaguchi
- Cancer and Developmental Biology Laboratory, National Cancer Institute, Frederick, MD 21702 USA (e-mail: )
| | - Tom Misteli
- Cell Biology of Genomes Group, National Cancer Institute, Bethesda, MD 20892 USA (e-mail: )
| | - Stephen J. Lockett
- Image Analysis Laboratory, Advanced Technology Program, SAIC—Frederick, National Cancer Institute, P.O. Box B, Frederick, MD 21702 USA (e-mail: )
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Automated, systematic determination of protein subcellular location using fluorescence microscopy. Subcell Biochem 2008; 43:263-76. [PMID: 17953398 DOI: 10.1007/978-1-4020-5943-8_12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
Proteomics is the comprehensive study of all aspects of protein behavior. The subfield of location proteomics is concerned with the systematic analysis of the subcellular location of proteins. In order to perform high-resolution, high-throughput analysis of all protein location patterns, automation is needed both for acquisition and analysis. Automated methods for analyzing subcellular location patterns in fluorescence microscope images have been developed and shown to work well for static 2D and 3D images of single cells. This chapter reviews this work and describes current efforts to extend these approaches, including classification of temporal patterns and building of generative models to represent location patterns.
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Paran Y, Ilan M, Kashman Y, Goldstein S, Liron Y, Geiger B, Kam Z. High-throughput screening of cellular features using high-resolution light-microscopy; Application for profiling drug effects on cell adhesion. J Struct Biol 2007; 158:233-43. [PMID: 17321150 DOI: 10.1016/j.jsb.2006.12.013] [Citation(s) in RCA: 31] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2006] [Revised: 08/29/2006] [Accepted: 12/18/2006] [Indexed: 11/17/2022]
Abstract
High-resolution light-microscopy and high-throughput screening are two essential methodologies for characterizing cellular phenotypes. Optimally combining these methodologies in cell-based screening to test detailed molecular and cellular responses to multiple perturbations constitutes a major challenge. Here we describe the development and application of a screening microscope platform that automatically acquires and interprets sub-micron resolution images at fast rates. The analysis pipeline is based on the quantification of multiple subcellular features and statistical comparisons of their distributions in treated vs. control cells. Using this platform, we screened 2200 natural extracts for their effects on the fine structure and organization of focal adhesions. This screen identified 15 effective extracts whose fractionation and characterization were further analyzed using the same microscope system. The significance of combining resolution, throughput and multi-parametric analyses for biomedical research and drug discovery is discussed.
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Affiliation(s)
- Yael Paran
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel
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Affiliation(s)
- Estelle Glory
- Center for Bioimage Informatics, Molecular Biosensor and Imaging Center, and Department of Biological Sciences, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA
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Zhao T, Murphy RF. Automated learning of generative models for subcellular location: Building blocks for systems biology. Cytometry A 2007; 71:978-90. [DOI: 10.1002/cyto.a.20487] [Citation(s) in RCA: 85] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Abstract
In the postgenomic era, to gain the most detailed quantitative data from biological specimens has become increasingly important in the emerging new fields of high-content and high-throughput single-cell analysis for systems biology and cytomics. Areas of research and diagnosis with the demand to virtually measure "anything" in the cell include immunophenotyping, rare cell detection and characterization in the case of stem cells and residual tumor cells, tissue analysis, and drug discovery. Systemic analysis is also a prerequisite for predictive medicine by genomics, proteomics, and cytomics. This issue of Cytometry Part A is dedicated to innovative concepts of system wide single cells analysis and manipulation, new technologies, data analysis and display, and, finally, quality assessment. The manuscripts to these chapters are provided by cutting edge experts in the fields. This overview will briefly highlight the most important aspects of this continuously developing field.
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Affiliation(s)
- Attila Tárnok
- Department of Pediatric Cardiology, Cardiac Center Leipzig GmbH, University of Leipzig, Germany.
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Fernández-González R, Muñoz-Barrutia A, Barcellos-Hoff MH, Ortiz-de-Solorzano C. Quantitative in vivo microscopy: the return from the 'omics'. Curr Opin Biotechnol 2006; 17:501-10. [PMID: 16899361 DOI: 10.1016/j.copbio.2006.07.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2006] [Revised: 06/21/2006] [Accepted: 07/28/2006] [Indexed: 11/28/2022]
Abstract
The confluence of recent advances in microscopy instrumentation and image analysis, coupled with the widespread use of GFP-like proteins as reporters of gene expression, has opened the door to high-throughput in vivo studies that can provide the morphological and temporal context to the biochemical pathways regulating cell function. We are now able to quantify the concentration and three-dimensional distribution of multiple spectrally resolved GFP-tagged proteins. Using automatic segmentation and tracking we can then measure the dynamics of the processes in which these elements are involved. In this way, parallel studies are feasible where multiple cell colonies treated with drugs or gene expression repressors can be monitored and analyzed to study the dynamics of relevant biological processes.
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Mittag A, Lenz D, Gerstner AOH, Tárnok A. Hyperchromatic cytometry principles for cytomics using slide based cytometry. Cytometry A 2006; 69:691-703. [PMID: 16680709 DOI: 10.1002/cyto.a.20285] [Citation(s) in RCA: 52] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
BACKGROUND Polychromatic analysis of biological specimens has become increasingly important because of the emerging new fields of high-content and high-throughput single cell analysis for systems biology and cytomics. Combining different technologies and staining methods, multicolor analysis can be pushed forward to measure anything stainable in a cell. We term this approach hyperchromatic cytometry and present different components suitable for achieving this task. For cell analysis, slide based cytometry (SBC) technologies are ideal as, unlike flow cytometry, they are non-consumptive, i.e. the analyzed sample is fixed on the slide and can be reanalyzed following restaining of the object. METHODS AND RESULTS We demonstrate various approaches for hyperchromatic analysis on a SBC instrument, the Laser Scanning Cytometer. The different components demonstrated here include (1) polychromatic cytometry (staining of the specimen with eight or more different fluorochromes simultaneously), (2) iterative restaining (using the same fluorochrome for restaining and subsequent reanalysis), (3) differential photobleaching (differentiating fluorochromes by their different photostability), (4) photoactivation (activating fluorescent nanoparticles or photocaged dyes), and (5) photodestruction (destruction of FRET dyes). Based on the ability to relocate cells that are immobilized on a microscope slide with a precision of approximately 1 microm, identical cells can be reanalyzed on the single cell level after manipulation steps. CONCLUSION With the intelligent combination of several different techniques, the hyperchromatic cytometry approach allows to quantify and analyze all components of relevance on the single cell level. The information gained per specimen is only limited by the number of available antibodies and sterical hindrance.
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Affiliation(s)
- Anja Mittag
- Department of Pediatric Cardiology, Cardiac Center Leipzig GmbH, University of Leipzig, Germany
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Baatz M, Arini N, Schäpe A, Binnig G, Linssen B. Object-oriented image analysis for high content screening: Detailed quantification of cells and sub cellular structures with the Cellenger software. Cytometry A 2006; 69:652-8. [PMID: 16680706 DOI: 10.1002/cyto.a.20289] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Detailed image analysis still is a considerable bottleneck for many cellular assays, and automated solutions to the problem are desirable. However, dealing with the complexity and variability of structures in cellular images makes detailed and reliable analysis a nontrivial task. METHODS Therefore, based on the object-oriented image analysis approach, a novel image analysis technology, a flexible and reliable system for image analysis in cellular assays was developed. It contains a library of predefined, adaptable modules, each of them developed for a specific analysis task. The system can be configured easily by combining appropriate modules and adapting them interactively to the specific image data, if necessary. By representing cells and sub cellular structures within a network of interlinked image objects, a large number of parameters can be derived that describe shape, intensity, and relevant structural and relational aspects of any chosen class of structures. RESULTS Thus, multi-parameter analysis and multiplexing are supported. A sample application based on this approach demonstrates that GFP signals can be distinguished based on their properties and the relative location within the cell.
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Abstract
Proteomics is a major current focus of biomedical research, and location proteomics is the important branch of proteomics that systematically studies the subcellular distributions for all proteins expressed in a given cell type. Fluorescence microscopy of labeled proteins is currently the main methodology to obtain location information. Traditionally, microscope images are analyzed by visual inspection, which suffers from inefficiency and inconsistency. Automated and objective interpretation approaches are therefore needed for location proteomics. In this article, we briefly review recent advances in automated imaging interpretation tools, including supervised classification (which assigns location pattern labels to previously unseen images), unsupervised clustering (which groups proteins based on the similarity among their subcellular distributions), and additional statistical tools that can aid cell and molecular biologists who use microscopy in their work.
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Affiliation(s)
- Xiang Chen
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
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