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Franklin ME, Bennett C, Arboite M, Alvarez-Ciara A, Corrales N, Verdelus J, Dietrich WD, Keane RW, de Rivero Vaccari JP, Prasad A. Activation of inflammasomes and their effects on neuroinflammation at the microelectrode-tissue interface in intracortical implants. Biomaterials 2023; 297:122102. [PMID: 37015177 PMCID: PMC10614166 DOI: 10.1016/j.biomaterials.2023.122102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 03/16/2023] [Accepted: 03/23/2023] [Indexed: 03/30/2023]
Abstract
Invasive neuroprosthetics rely on microelectrodes (MEs) to record or stimulate the activity of large neuron assemblies. However, MEs are subjected to tissue reactivity in the central nervous system (CNS) due to the foreign body response (FBR) that contribute to chronic neuroinflammation and ultimately result in ME failure. An endogenous, acute set of mechanisms responsible for the recognition and targeting of foreign objects, called the innate immune response, immediately follows the ME implant-induced trauma. Inflammasomes are multiprotein structures that play a critical role in the initiation of an innate immune response following CNS injuries. The activation of inflammasomes facilitates a range of innate immune response cascades and results in neuroinflammation and programmed cell death. Despite our current understanding of inflammasomes, their roles in the context of neural device implantation remain unknown. In this study, we implanted a non-functional Utah electrode array (UEA) into the rat somatosensory cortex and studied the inflammasome signaling and the corresponding downstream effects on inflammatory cytokine expression and the inflammasome-mediated cell death mechanism of pyroptosis. Our results not only demonstrate the continuous activation of inflammasomes and their contribution to neuroinflammation at the electrode-tissue interface but also reveal the therapeutic potential of targeting inflammasomes to attenuate the FBR in invasive neuroprosthetics.
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Affiliation(s)
- Melissa E Franklin
- Department of Biomedical Engineering, University of Miami, Miami, FL, USA
| | - Cassie Bennett
- Department of Biomedical Engineering, University of Miami, Miami, FL, USA
| | - Maelle Arboite
- Department of Biomedical Engineering, University of Miami, Miami, FL, USA
| | | | - Natalie Corrales
- Department of Biomedical Engineering, University of Miami, Miami, FL, USA
| | - Jennifer Verdelus
- Department of Biomedical Engineering, University of Miami, Miami, FL, USA
| | - W Dalton Dietrich
- Department of Biomedical Engineering, University of Miami, Miami, FL, USA; Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, USA; The Miami Project to Cure Paralysis, University of Miami, Miami, FL, USA
| | - Robert W Keane
- The Miami Project to Cure Paralysis, University of Miami, Miami, FL, USA; Department of Physiology and Biophysics, University of Miami Miller School of Medicine, Miami, FL, USA; Center for Cognitive Neuroscience and Aging University of Miami Miller School of Medicine, Miami, FL, USA
| | - Juan Pablo de Rivero Vaccari
- The Miami Project to Cure Paralysis, University of Miami, Miami, FL, USA; Department of Physiology and Biophysics, University of Miami Miller School of Medicine, Miami, FL, USA; Center for Cognitive Neuroscience and Aging University of Miami Miller School of Medicine, Miami, FL, USA
| | - Abhishek Prasad
- Department of Biomedical Engineering, University of Miami, Miami, FL, USA; The Miami Project to Cure Paralysis, University of Miami, Miami, FL, USA.
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2
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Li W, Zhang X, Stern A, Birtwistle M, Iuricich F. CellTrackVis: analyzing the performance of cell tracking algorithms. EUROGRAPHICS/IEEE VGTC SYMPOSIUM ON VISUALIZATION : EUROVIS : [PROCEEDINGS]. EUROGRAPHICS/IEEE VGTC SYMPOSIUM ON VISUALIZATION 2022; 2022:115-119. [PMID: 36656607 PMCID: PMC9841471 DOI: 10.2312/evs.20221103] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Live-cell imaging is a common data acquisition technique used by biologists to analyze cell behavior. Since manually tracking cells in a video sequence is extremely time-consuming, many automatic algorithms have been developed in the last twenty years to accomplish the task. However, none of these algorithms can yet claim robust tracking performance at the varying of acquisition conditions (e.g., cell type, acquisition device, cell treatments). While many visualization tools exist to help with cell behavior analysis, there are no tools to help with the algorithm's validation. This paper proposes CellTrackVis, a new visualization tool for evaluating cell tracking algorithms. CellTrackVis allows comparing automatically generated cell tracks with ground truth data to help biologists select the best-suited algorithm for their experimented pipeline. Moreover, CellTackVis can be used as a debugging tool while developing a new cell tracking algorithm to investigate where, when, and why each tracking error occurred.
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Affiliation(s)
- W. Li
- School of Computing, Clemson University, United States
| | - X. Zhang
- School of Computing, Clemson University, United States
| | - A. Stern
- Icahn School of Medicine at Mount Sinai, New York, United States
| | - M. Birtwistle
- Department of Chemical and Biomolecular Engineering, Clemson University, United States
| | - F. Iuricich
- School of Computing, Clemson University, United States
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3
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Lange D, Polanco E, Judson-Torres R, Zangle T, Lex A. Loon: Using Exemplars to Visualize Large-Scale Microscopy Data. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2022; 28:248-258. [PMID: 34587022 DOI: 10.1109/tvcg.2021.3114766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Which drug is most promising for a cancer patient? A new microscopy-based approach for measuring the mass of individual cancer cells treated with different drugs promises to answer this question in only a few hours. However, the analysis pipeline for extracting data from these images is still far from complete automation: human intervention is necessary for quality control for preprocessing steps such as segmentation, adjusting filters, removing noise, and analyzing the result. To address this workflow, we developed Loon, a visualization tool for analyzing drug screening data based on quantitative phase microscopy imaging. Loon visualizes both derived data such as growth rates and imaging data. Since the images are collected automatically at a large scale, manual inspection of images and segmentations is infeasible. However, reviewing representative samples of cells is essential, both for quality control and for data analysis. We introduce a new approach for choosing and visualizing representative exemplar cells that retain a close connection to the low-level data. By tightly integrating the derived data visualization capabilities with the novel exemplar visualization and providing selection and filtering capabilities, Loon is well suited for making decisions about which drugs are suitable for a specific patient.
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4
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Veschini L, Sailem H, Malani D, Pietiäinen V, Stojiljkovic A, Wiseman E, Danovi D. High-Content Imaging to Phenotype Human Primary and iPSC-Derived Cells. Methods Mol Biol 2021; 2185:423-445. [PMID: 33165865 DOI: 10.1007/978-1-0716-0810-4_27] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Increasingly powerful microscopy, liquid handling, and computational techniques have enabled cell imaging in high throughput. Microscopy images are quantified using high-content analysis platforms linking object features to cell behavior. This can be attempted on physiologically relevant cell models, including stem cells and primary cells, in complex environments, and conceivably in the presence of perturbations. Recently, substantial focus has been devoted to cell profiling for cell therapy, assays for drug discovery or biomarker identification for clinical decision-making protocols, bringing this wealth of information into translational applications. In this chapter, we focus on two protocols enabling to (1) benchmark human cells, in particular human endothelial cells as a case study and (2) extract cells from blood for follow-up experiments including image-based drug testing. We also present concepts of high-content imaging and discuss the benefits and challenges, with the aim of enabling readers to tailor existing pipelines and bring such approaches closer to translational research and the clinic.
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Affiliation(s)
- Lorenzo Veschini
- Academic Centre of Reconstructive Science, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK
| | - Heba Sailem
- The Institute of Biomedical Engineering, Oxford, UK
| | - Disha Malani
- Institute for Molecular Medicine Finland-FIMM, Helsinki Institute of Life Science-HiLIFE, University of Helsinki, Helsinki, Finland
| | - Vilja Pietiäinen
- Institute for Molecular Medicine Finland-FIMM, Helsinki Institute of Life Science-HiLIFE, University of Helsinki, Helsinki, Finland
| | - Ana Stojiljkovic
- Division of Veterinary Anatomy, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Erika Wiseman
- Stem Cell Hotel, Centre for Stem Cells and Regenerative Medicine, King's College London, London, UK
| | - Davide Danovi
- Stem Cell Hotel, Centre for Stem Cells and Regenerative Medicine, King's College London, London, UK.
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5
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Biot E, Crowell E, Burguet J, Höfte H, Vernhettes S, Andrey P. Strategy and software for the statistical spatial analysis of 3D intracellular distributions. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2016; 87:230-242. [PMID: 27121260 DOI: 10.1111/tpj.13189] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2015] [Revised: 04/05/2016] [Accepted: 04/07/2016] [Indexed: 06/05/2023]
Abstract
The localization of proteins in specific domains or compartments in the 3D cellular space is essential for many fundamental processes in eukaryotic cells. Deciphering spatial organization principles within cells is a challenging task, in particular because of the large morphological variations between individual cells. We present here an approach for normalizing variations in cell morphology and for statistically analyzing spatial distributions of intracellular compartments from collections of 3D images. The method relies on the processing and analysis of 3D geometrical models that are generated from image stacks and that are used to build representations at progressively increasing levels of integration, ultimately revealing statistical significant traits of spatial distributions. To make this methodology widely available to end-users, we implemented our algorithmic pipeline into a user-friendly, multi-platform, and freely available software. To validate our approach, we generated 3D statistical maps of endomembrane compartments at subcellular resolution within an average epidermal root cell from collections of image stacks. This revealed unsuspected polar distribution patterns of organelles that were not detectable in individual images. By reversing the classical 'measure-then-average' paradigm, one major benefit of the proposed strategy is the production and display of statistical 3D representations of spatial organizations, thus fully preserving the spatial dimension of image data and at the same time allowing their integration over individual observations. The approach and software are generic and should be of general interest for experimental and modeling studies of spatial organizations at multiple scales (subcellular, cellular, tissular) in biological systems.
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Affiliation(s)
- Eric Biot
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, RD10, 78000, Versailles, France
| | - Elizabeth Crowell
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, RD10, 78000, Versailles, France
| | - Jasmine Burguet
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, RD10, 78000, Versailles, France
- INRA, Neurobiologie de l'Olfaction, UR1197, F-78350, Jouy-en-Josas, France
| | - Herman Höfte
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, RD10, 78000, Versailles, France
| | - Samantha Vernhettes
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, RD10, 78000, Versailles, France
| | - Philippe Andrey
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, RD10, 78000, Versailles, France
- INRA, Neurobiologie de l'Olfaction, UR1197, F-78350, Jouy-en-Josas, France
- Sorbonne Universités, UPMC Univ Paris 06, UFR927, F-75005, Paris, France
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6
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Lockett S, Verma C, Brafman A, Gudla P, Nandy K, Mimaki Y, Fuchs PL, Jaja J, Reilly KM, Beutler J, Turbyville TJ. Quantitative analysis of F-actin redistribution in astrocytoma cells treated with candidate pharmaceuticals. Cytometry A 2014; 85:512-21. [PMID: 24515854 PMCID: PMC4385705 DOI: 10.1002/cyto.a.22442] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2013] [Revised: 11/21/2013] [Accepted: 12/27/2013] [Indexed: 01/17/2023]
Abstract
Actin fibers (F-actin) control the shape and internal organization of cells, and generate force. It has been long appreciated that these functions are tightly coupled, and in some cases drive cell behavior and cell fate. The distribution and dynamics of F-actin is different in cancer versus normal cells and in response to small molecules, including actin-targeting natural products and anticancer drugs. Therefore, quantifying actin structural changes from high resolution fluorescence micrographs is necessary for further understanding actin cytoskeleton dynamics and phenotypic consequences of drug interactions on cells. We applied an artificial neural network algorithm, which used image intensity and anisotropy measurements, to quantitatively classify F-actin subcellular features into actin along the edges of cells, actin at the protrusions of cells, internal fibers and punctate signals. The algorithm measured significant increase in F-actin at cell edges with concomitant decrease in internal punctate actin in astrocytoma cells lacking functional neurofibromin and p53 when treated with three structurally-distinct anticancer small molecules: OSW1, Schweinfurthin A (SA) and a synthetic marine compound 23'-dehydroxycephalostatin 1. Distinctly different changes were measured in cells treated with the actin inhibitor cytochalasin B. These measurements support published reports that SA acts on F-actin in NF1(-/-) neurofibromin deficient cancer cells through changes in Rho signaling. Quantitative pattern analysis of cells has wide applications for understanding mechanisms of small molecules, because many anti-cancer drugs directly or indirectly target cytoskeletal proteins. Furthermore, quantitative information about the actin cytoskeleton may make it possible to further understand cell fate decisions using mathematically testable models.
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Affiliation(s)
- Stephen Lockett
- Optical Microscopy and Analysis Laboratory, Frederick National Laboratory for Cancer Research (FNLCR), Leidos Biomedical Research Inc, Frederick, Maryland
| | | | - Alla Brafman
- Optical Microscopy and Analysis Laboratory, Frederick National Laboratory for Cancer Research (FNLCR), Leidos Biomedical Research Inc, Frederick, Maryland
| | - Prabhakar Gudla
- Optical Microscopy and Analysis Laboratory, Frederick National Laboratory for Cancer Research (FNLCR), Leidos Biomedical Research Inc, Frederick, Maryland
| | - Kaustav Nandy
- Optical Microscopy and Analysis Laboratory, Frederick National Laboratory for Cancer Research (FNLCR), Leidos Biomedical Research Inc, Frederick, Maryland
| | - Yoshihiro Mimaki
- School of Pharmacy, Tokyo University of Pharmacy and Life Sciences, Hachioji, Tokyo, 192-0392, Japan
| | - Philip L. Fuchs
- Department of Chemistry, Purdue University, West Lafayette, Indiana, 47907
| | - Joseph Jaja
- Electrical and Computer Engineering, University of Maryland, College Park, Maryland
| | - Karlyne M. Reilly
- Mouse Cancer Genetics Program, National Cancer Institute—Frederick (NCI-F), Frederick, Maryland
| | - John Beutler
- Molecular Targets Laboratory, NCI-F, Frederick, Maryland
| | - Thomas J. Turbyville
- Optical Microscopy and Analysis Laboratory, Frederick National Laboratory for Cancer Research (FNLCR), Leidos Biomedical Research Inc, Frederick, Maryland
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7
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Pavie B, Rajaram S, Ouyang A, Altschuler JM, Steininger RJ, Wu LF, Altschuler SJ. Rapid analysis and exploration of fluorescence microscopy images. J Vis Exp 2014. [PMID: 24686220 DOI: 10.3791/51280] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized image analysis tools are available, most traditional image-analysis-based workflows have steep learning curves (for fine tuning of analysis parameters) and result in long turnaround times between imaging and analysis. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard. Here we present an alternate, cell-segmentation-free workflow based on PhenoRipper, an open-source software platform designed for the rapid analysis and exploration of microscopy images. The pipeline presented here is optimized for immunofluorescence microscopy images of cell cultures and requires minimal user intervention. Within half an hour, PhenoRipper can analyze data from a typical 96-well experiment and generate image profiles. Users can then visually explore their data, perform quality control on their experiment, ensure response to perturbations and check reproducibility of replicates. This facilitates a rapid feedback cycle between analysis and experiment, which is crucial during assay optimization. This protocol is useful not just as a first pass analysis for quality control, but also may be used as an end-to-end solution, especially for screening. The workflow described here scales to large data sets such as those generated by high-throughput screens, and has been shown to group experimental conditions by phenotype accurately over a wide range of biological systems. The PhenoBrowser interface provides an intuitive framework to explore the phenotypic space and relate image properties to biological annotations. Taken together, the protocol described here will lower the barriers to adopting quantitative analysis of image based screens.
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Affiliation(s)
- Benjamin Pavie
- Green Center for Systems Biology, UT Southwestern Medical Center
| | - Satwik Rajaram
- Green Center for Systems Biology, UT Southwestern Medical Center
| | - Austin Ouyang
- Advanced Imaging Research Center, UT Southwestern Medical Center
| | - Jason M Altschuler
- Green Center for Systems Biology, UT Southwestern Medical Center; Princeton University
| | | | - Lani F Wu
- Green Center for Systems Biology, UT Southwestern Medical Center;
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8
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Different approaches for extracting information from the co-occurrence matrix. PLoS One 2013; 8:e83554. [PMID: 24386228 PMCID: PMC3873395 DOI: 10.1371/journal.pone.0083554] [Citation(s) in RCA: 61] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2013] [Accepted: 11/05/2013] [Indexed: 02/06/2023] Open
Abstract
In 1979 Haralick famously introduced a method for analyzing the texture of an image: a set of statistics extracted from the co-occurrence matrix. In this paper we investigate novel sets of texture descriptors extracted from the co-occurrence matrix; in addition, we compare and combine different strategies for extending these descriptors. The following approaches are compared: the standard approach proposed by Haralick, two methods that consider the co-occurrence matrix as a three-dimensional shape, a gray-level run-length set of features and the direct use of the co-occurrence matrix projected onto a lower dimensional subspace by principal component analysis. Texture descriptors are extracted from the co-occurrence matrix evaluated at multiple scales. Moreover, the descriptors are extracted not only from the entire co-occurrence matrix but also from subwindows. The resulting texture descriptors are used to train a support vector machine and ensembles. Results show that our novel extraction methods improve the performance of standard methods. We validate our approach across six medical datasets representing different image classification problems using the Wilcoxon signed rank test. The source code used for the approaches tested in this paper will be available at: http://www.dei.unipd.it/wdyn/?IDsezione=3314&IDgruppo_pass=124&preview=.
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9
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Sommer C, Gerlich DW. Machine learning in cell biology - teaching computers to recognize phenotypes. J Cell Sci 2013; 126:5529-39. [PMID: 24259662 DOI: 10.1242/jcs.123604] [Citation(s) in RCA: 219] [Impact Index Per Article: 19.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Recent advances in microscope automation provide new opportunities for high-throughput cell biology, such as image-based screening. High-complex image analysis tasks often make the implementation of static and predefined processing rules a cumbersome effort. Machine-learning methods, instead, seek to use intrinsic data structure, as well as the expert annotations of biologists to infer models that can be used to solve versatile data analysis tasks. Here, we explain how machine-learning methods work and what needs to be considered for their successful application in cell biology. We outline how microscopy images can be converted into a data representation suitable for machine learning, and then introduce various state-of-the-art machine-learning algorithms, highlighting recent applications in image-based screening. Our Commentary aims to provide the biologist with a guide to the application of machine learning to microscopy assays and we therefore include extensive discussion on how to optimize experimental workflow as well as the data analysis pipeline.
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Affiliation(s)
- Christoph Sommer
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), 1030 Vienna, Austria
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10
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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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11
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Li C, Wang XH, Zheng L, Huang JF. Automated protein subcellular localization based on local invariant features. Protein J 2013; 32:230-7. [PMID: 23512411 DOI: 10.1007/s10930-013-9478-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
To understand the function of the encoded proteins, we need to be able to know the subcellular location of a protein. The most common method used for determining subcellular location is fluorescence microscopy which allows subcellular localizations to be imaged in high throughput. Image feature calculation has proven invaluable in the automated analysis of cellular images. This article proposes a novel method named LDPs for feature extraction based on invariant of translation and rotation from given images, the nature which is to count the local difference features of images, and the difference features are given by calculating the D-value between the gray value of the central pixel c and the gray values of eight pixels in the neighborhood. The novel method is tested on two image sets, the first set is which fluorescently tagged protein was endogenously expressed in 10 sebcellular locations, and the second set is which protein was transfected in 11 locations. A SVM was trained and tested for each image set and classification accuracies of 96.7 and 92.3 % were obtained on the endogenous and transfected sets respectively.
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Affiliation(s)
- Chao Li
- Department of Computer Science and Technology, Shanghai Normal University, Shanghai, 200234, China
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12
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Kutsuna N, Higaki T, Matsunaga S, Otsuki T, Yamaguchi M, Fujii H, Hasezawa S. Active learning framework with iterative clustering for bioimage classification. Nat Commun 2013; 3:1032. [PMID: 22929789 PMCID: PMC3432472 DOI: 10.1038/ncomms2030] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2012] [Accepted: 07/30/2012] [Indexed: 11/19/2022] Open
Abstract
Advances in imaging systems have yielded a flood of images into the research field. A semi-automated facility can reduce the laborious task of classifying this large number of images. Here we report the development of a novel framework, CARTA (Clustering-Aided Rapid Training Agent), applicable to bioimage classification that facilitates annotation and selection of features. CARTA comprises an active learning algorithm combined with a genetic algorithm and self-organizing map. The framework provides an easy and interactive annotation method and accurate classification. The CARTA framework enables classification of subcellular localization, mitotic phases and discrimination of apoptosis in images of plant and human cells with an accuracy level greater than or equal to annotators. CARTA can be applied to classification of magnetic resonance imaging of cancer cells or multicolour time-course images after surgery. Furthermore, CARTA can support development of customized features for classification, high-throughput phenotyping and application of various classification schemes dependent on the user's purpose. Semi-automated imaging systems help with the task of classifying large numbers of biological images. This study presents a novel framework—CARTA—with an active learning algorithm combined with a genetic algorithm, whose applications include the classification of magnetic resonance imaging of cancer cells.
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Affiliation(s)
- Natsumaro Kutsuna
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Chiba 277-8562, Japan
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13
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Abstract
As microscopy becomes increasingly automated and imaging expands in the spatial and time dimensions, quantitative analysis tools for fluorescent imaging are becoming critical to remove both bottlenecks in throughput as well as fully extract and exploit the information contained in the imaging. In recent years there has been a flurry of activity in the development of bio-image analysis tools and methods with the result that there are now many high-quality, well-documented, and well-supported open source bio-image analysis projects with large user bases that cover essentially every aspect from image capture to publication. These open source solutions are now providing a viable alternative to commercial solutions. More importantly, they are forming an interoperable and interconnected network of tools that allow data and analysis methods to be shared between many of the major projects. Just as researchers build on, transmit, and verify knowledge through publication, open source analysis methods and software are creating a foundation that can be built upon, transmitted, and verified. Here we describe many of the major projects, their capabilities, and features. We also give an overview of the current state of open source software for fluorescent microscopy analysis and the many reasons to use and develop open source methods.
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14
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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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15
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Walter T, Shattuck DW, Baldock R, Bastin ME, Carpenter AE, Duce S, Ellenberg J, Fraser A, Hamilton N, Pieper S, Ragan MA, Schneider JE, Tomancak P, Hériché JK. Visualization of image data from cells to organisms. Nat Methods 2010; 7:S26-41. [PMID: 20195255 PMCID: PMC3650473 DOI: 10.1038/nmeth.1431] [Citation(s) in RCA: 247] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Advances in imaging techniques and high-throughput technologies are providing scientists with unprecedented possibilities to visualize internal structures of cells, organs and organisms and to collect systematic image data characterizing genes and proteins on a large scale. To make the best use of these increasingly complex and large image data resources, the scientific community must be provided with methods to query, analyze and crosslink these resources to give an intuitive visual representation of the data. This review gives an overview of existing methods and tools for this purpose and highlights some of their limitations and challenges.
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Affiliation(s)
- Thomas Walter
- European Molecular Biology Laboratory, Heidelberg, Germany
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