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Seo B, Lee D, Jeon H, Ha J, Suh S. MotGen: a closed-loop bacterial motility control framework using generative adversarial networks. Bioinformatics 2024; 40:btae170. [PMID: 38552318 PMCID: PMC11031359 DOI: 10.1093/bioinformatics/btae170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 03/02/2024] [Accepted: 03/27/2024] [Indexed: 04/21/2024] Open
Abstract
MOTIVATION Many organisms' survival and behavior hinge on their responses to environmental signals. While research on bacteria-directed therapeutic agents has increased, systematic exploration of real-time modulation of bacterial motility remains limited. Current studies often focus on permanent motility changes through genetic alterations, restricting the ability to modulate bacterial motility dynamically on a large scale. To address this gap, we propose a novel real-time control framework for systematically modulating bacterial motility dynamics. RESULTS We introduce MotGen, a deep learning approach leveraging Generative Adversarial Networks to analyze swimming performance statistics of motile bacteria based on live cell imaging data. By tracking objects and optimizing cell trajectory mapping under environmentally altered conditions, we trained MotGen on a comprehensive statistical dataset derived from real image data. Our experimental results demonstrate MotGen's ability to capture motility dynamics from real bacterial populations with low mean absolute error in both simulated and real datasets. MotGen allows us to approach optimal swimming conditions for desired motility statistics in real-time. MotGen's potential extends to practical biomedical applications, including immune response prediction, by providing imputation of bacterial motility patterns based on external environmental conditions. Our short-term, in-situ interventions for controlling motility behavior offer a promising foundation for the development of bacteria-based biomedical applications. AVAILABILITY AND IMPLEMENTATION MotGen is presented as a combination of Matlab image analysis code and a machine learning workflow in Python. Codes are available at https://github.com/bgmseo/MotGen, for cell tracking and implementation of trained models to generate bacterial motility statistics.
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Affiliation(s)
- BoGeum Seo
- Department of Mechanical Engineering, Seoul National University, 08826 Seoul, Republic of Korea
| | - DoHee Lee
- Center for Healthcare Robotics, Korea Institute of Science & Technology, 02792 Seoul, Republic of Korea
| | - Heungjin Jeon
- Infection Control Convergence Research Center, Chungnam National University, 34134 Daejeon, Republic of Korea
| | - Junhyoung Ha
- Center for Healthcare Robotics, Korea Institute of Science & Technology, 02792 Seoul, Republic of Korea
| | - SeungBeum Suh
- Center for Healthcare Robotics, Korea Institute of Science & Technology, 02792 Seoul, Republic of Korea
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2
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Song T, Choi Y, Jeon JH, Cho YK. A machine learning approach to discover migration modes and transition dynamics of heterogeneous dendritic cells. Front Immunol 2023; 14:1129600. [PMID: 37081879 PMCID: PMC10110959 DOI: 10.3389/fimmu.2023.1129600] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 03/06/2023] [Indexed: 04/07/2023] Open
Abstract
Dendritic cell (DC) migration is crucial for mounting immune responses. Immature DCs (imDCs) reportedly sense infections, while mature DCs (mDCs) move quickly to lymph nodes to deliver antigens to T cells. However, their highly heterogeneous and complex innate motility remains elusive. Here, we used an unsupervised machine learning (ML) approach to analyze long-term, two-dimensional migration trajectories of Granulocyte-macrophage colony-stimulating factor (GMCSF)-derived bone marrow-derived DCs (BMDCs). We discovered three migratory modes independent of the cell state: slow-diffusive (SD), slow-persistent (SP), and fast-persistent (FP). Remarkably, imDCs more frequently changed their modes, predominantly following a unicyclic SD→FP→SP→SD transition, whereas mDCs showed no transition directionality. We report that DC migration exhibits a history-dependent mode transition and maturation-dependent motility changes are emergent properties of the dynamic switching of the three migratory modes. Our ML-based investigation provides new insights into studying complex cellular migratory behavior.
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Affiliation(s)
- Taegeun Song
- Department of Physics, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea
- Department of Data information and Physics, Kongju National University, Gongju, Republic of Korea
| | - Yongjun Choi
- Center for Soft and Living Matter, Institute for Basic Science (IBS), Ulsan, Republic of Korea
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea
| | - Jae-Hyung Jeon
- Department of Physics, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea
- Asia Pacific Center for Theoretical Physics (APCTP), Pohang, Republic of Korea
- *Correspondence: Jae-Hyung Jeon, ; Yoon-Kyoung Cho,
| | - Yoon-Kyoung Cho
- Center for Soft and Living Matter, Institute for Basic Science (IBS), Ulsan, Republic of Korea
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea
- *Correspondence: Jae-Hyung Jeon, ; Yoon-Kyoung Cho,
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Choi HJ, Wang C, Pan X, Jang J, Cao M, Brazzo JA, Bae Y, Lee K. Emerging machine learning approaches to phenotyping cellular motility and morphodynamics. Phys Biol 2021; 18:10.1088/1478-3975/abffbe. [PMID: 33971636 PMCID: PMC9131244 DOI: 10.1088/1478-3975/abffbe] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 05/10/2021] [Indexed: 12/22/2022]
Abstract
Cells respond heterogeneously to molecular and environmental perturbations. Phenotypic heterogeneity, wherein multiple phenotypes coexist in the same conditions, presents challenges when interpreting the observed heterogeneity. Advances in live cell microscopy allow researchers to acquire an unprecedented amount of live cell image data at high spatiotemporal resolutions. Phenotyping cellular dynamics, however, is a nontrivial task and requires machine learning (ML) approaches to discern phenotypic heterogeneity from live cell images. In recent years, ML has proven instrumental in biomedical research, allowing scientists to implement sophisticated computation in which computers learn and effectively perform specific analyses with minimal human instruction or intervention. In this review, we discuss how ML has been recently employed in the study of cell motility and morphodynamics to identify phenotypes from computer vision analysis. We focus on new approaches to extract and learn meaningful spatiotemporal features from complex live cell images for cellular and subcellular phenotyping.
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Affiliation(s)
- Hee June Choi
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, United States of America
- Vascular Biology Program and Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States of America
| | - Chuangqi Wang
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, United States of America
- Present address. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Xiang Pan
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, United States of America
- Vascular Biology Program and Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States of America
| | - Junbong Jang
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, United States of America
- Vascular Biology Program and Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States of America
| | - Mengzhi Cao
- Data Science Program, Worcester Polytechnic Institute, Worcester, MA 01609, United States of America
| | - Joseph A Brazzo
- Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY 14203, United States of America
| | - Yongho Bae
- Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY 14203, United States of America
| | - Kwonmoo Lee
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, United States of America
- Vascular Biology Program and Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States of America
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Kimmel JC, Brack AS, Marshall WF. Deep Convolutional and Recurrent Neural Networks for Cell Motility Discrimination and Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:562-574. [PMID: 31251191 DOI: 10.1109/tcbb.2019.2919307] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Cells in culture display diverse motility behaviors that may reflect differences in cell state and function, providing motivation to discriminate between different motility behaviors. Current methods to do so rely upon manual feature engineering. However, the types of features necessary to distinguish between motility behaviors can vary greatly depending on the biological context, and it is not always clear which features may be most predictive in each setting for distinguishing particular cell types or disease states. Convolutional neural networks (CNNs) are machine learning models allowing for relevant features to be learned directly from spatial data. Similarly, recurrent neural networks (RNNs) are a class of models capable of learning long term temporal dependencies. Given that cell motility is inherently spacio-temporal data, we present an approach utilizing both convolutional and long- short-term memory (LSTM) recurrent neural network units to analyze cell motility data. These RNN models provide accurate classification of simulated motility and experimentally measured motility from multiple cell types, comparable to results achieved with hand-engineered features. The variety of cell motility differences we can detect suggests that the algorithm is generally applicable to additional cell types not analyzed here. RNN autoencoders based on the same architecture are capable of learning motility features in an unsupervised manner and capturing variation between myogenic cells in the latent space. Adapting these RNN models to motility prediction, RNNs are capable of predicting muscle stem cell motility from past tracking data with performance superior to standard motion prediction models. This advance in cell motility prediction may be of practical utility in cell tracking applications.
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Accelerating the experimental responses on cell behaviors: a long-term prediction of cell trajectories using Social Generative Adversarial Network. Sci Rep 2020; 10:15635. [PMID: 32973301 PMCID: PMC7519062 DOI: 10.1038/s41598-020-72605-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 07/17/2020] [Indexed: 01/04/2023] Open
Abstract
The incremented uptake provided by time-lapse microscopy in Organ-on-a-Chip (OoC) devices allowed increased attention to the dynamics of the co-cultured systems. However, the amount of information stored in long-time experiments may constitute a serious bottleneck of the experimental pipeline. Forward long-term prediction of cell trajectories may reduce the spatial–temporal burden of video sequences storage. Cell trajectory prediction becomes crucial especially to increase the trustworthiness in software tools designed to conduct a massive analysis of cell behavior under chemical stimuli. To address this task, we transpose here the exploitation of the presence of “social forces” from the human to the cellular level for motion prediction at microscale by adapting the potential of Social Generative Adversarial Network predictors to cell motility. To demonstrate the effectiveness of the approach, we consider here two case studies: one related to PC-3 prostate cancer cells cultured in 2D Petri dishes under control and treated conditions and one related to an OoC experiment of tumor-immune interaction in fibrosarcoma cells. The goodness of the proposed strategy has been verified by successfully comparing the distributions of common descriptors (kinematic descriptors and mean interaction time for the two scenarios respectively) from the trajectories obtained by video analysis and the predicted counterparts.
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Solís-Lemus JA, Sánchez-Sánchez BJ, Marcotti S, Burki M, Stramer B, Reyes-Aldasoro CC. Comparative Study of Contact Repulsion in Control and Mutant Macrophages Using a Novel Interaction Detection. J Imaging 2020; 6:36. [PMID: 34460738 PMCID: PMC8321020 DOI: 10.3390/jimaging6050036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 05/01/2020] [Accepted: 05/15/2020] [Indexed: 11/16/2022] Open
Abstract
In this paper, a novel method for interaction detection is presented to compare the contact dynamics of macrophages in the Drosophila embryo. The study is carried out by a framework called macrosight, which analyses the movement and interaction of migrating macrophages. The framework incorporates a segmentation and tracking algorithm into analysing the motion characteristics of cells after contact. In this particular study, the interactions between cells is characterised in the case of control embryos and Shot mutants, a candidate protein that is hypothesised to regulate contact dynamics between migrating cells. Statistical significance between control and mutant cells was found when comparing the direction of motion after contact in specific conditions. Such discoveries provide insights for future developments in combining biological experiments with computational analysis.
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Affiliation(s)
- José Alonso Solís-Lemus
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Besaiz J Sánchez-Sánchez
- Randall Centre for Cell & Molecular Biophysics, King’s College London, London SE1 1UL, UK; (B.J.S.-S.); (S.M.); (M.B.); (B.S.)
| | - Stefania Marcotti
- Randall Centre for Cell & Molecular Biophysics, King’s College London, London SE1 1UL, UK; (B.J.S.-S.); (S.M.); (M.B.); (B.S.)
| | - Mubarik Burki
- Randall Centre for Cell & Molecular Biophysics, King’s College London, London SE1 1UL, UK; (B.J.S.-S.); (S.M.); (M.B.); (B.S.)
| | - Brian Stramer
- Randall Centre for Cell & Molecular Biophysics, King’s College London, London SE1 1UL, UK; (B.J.S.-S.); (S.M.); (M.B.); (B.S.)
| | - Constantino Carlos Reyes-Aldasoro
- GiCentre, Departmen t of Computer Science, School of Mathematics, Computer Science and Engineering, City, University of London, London EC1V 0HB, UK
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Solís-Lemus JA, Stramer B, Slabaugh G, Reyes-Aldasoro CC. Macrosight: A Novel Framework to Analyze the Shape and Movement of Interacting Macrophages Using Matlab ®. J Imaging 2019; 5:jimaging5010017. [PMID: 34465701 PMCID: PMC8320860 DOI: 10.3390/jimaging5010017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 01/05/2019] [Accepted: 01/08/2019] [Indexed: 11/26/2022] Open
Abstract
This paper presents a novel software framework, called macrosight, which incorporates routines to detect, track, and analyze the shape and movement of objects, with special emphasis on macrophages. The key feature presented in macrosight consists of an algorithm to assess the changes of direction derived from cell–cell contact, where an interaction is assumed to occur. The main biological motivation is the determination of certain cell interactions influencing cell migration. Thus, the main objective of this work is to provide insights into the notion that interactions between cell structures cause a change in orientation. Macrosight analyzes the change of direction of cells before and after they come in contact with another cell. Interactions are determined when the cells overlap and form clumps of two or more cells. The framework integrates a segmentation technique capable of detecting overlapping cells and a tracking framework into a tool for the analysis of the trajectories of cells before and after they overlap. Preliminary results show promise into the analysis and the hypothesis proposed, and lays the groundwork for further developments. The extensive experimentation and data analysis show, with statistical significance, that under certain conditions, the movement changes before and after an interaction are different from movement in controlled cases.
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Affiliation(s)
- José Alonso Solís-Lemus
- School of Mathematics, Computer Science and Engineering, City, University of London, London EC1V 0HB, UK
- Correspondence: or
| | - Brian Stramer
- Randall Division of Cell & Molecular Biophysics, King’s College London, London WC2R 2LS, UK
| | - Greg Slabaugh
- School of Mathematics, Computer Science and Engineering, City, University of London, London EC1V 0HB, UK
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Zaritsky A. Sharing and reusing cell image data. Mol Biol Cell 2018; 29:1274-1280. [PMID: 29851565 PMCID: PMC5994892 DOI: 10.1091/mbc.e17-10-0606] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Revised: 04/02/2018] [Accepted: 04/06/2018] [Indexed: 01/19/2023] Open
Abstract
The rapid growth in content and complexity of cell image data creates an opportunity for synergy between experimental and computational scientists. Sharing microscopy data enables computational scientists to develop algorithms and tools for data analysis, integration, and mining. These tools can be applied by experimentalists to promote hypothesis-generation and discovery. We are now at the dawn of this revolution: infrastructure is being developed for data standardization, deposition, sharing, and analysis; some journals and funding agencies mandate data deposition; data journals publish high-content microscopy data sets; quantification becomes standard in scientific publications; new analytic tools are being developed and dispatched to the community; and huge data sets are being generated by individual labs and philanthropic initiatives. In this Perspective, I reflect on sharing and reusing cell image data and the opportunities that will come along with it.
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Kimmel JC, Chang AY, Brack AS, Marshall WF. Inferring cell state by quantitative motility analysis reveals a dynamic state system and broken detailed balance. PLoS Comput Biol 2018; 14:e1005927. [PMID: 29338005 PMCID: PMC5786322 DOI: 10.1371/journal.pcbi.1005927] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 01/26/2018] [Accepted: 12/13/2017] [Indexed: 02/02/2023] Open
Abstract
Cell populations display heterogeneous and dynamic phenotypic states at multiple scales. Similar to molecular features commonly used to explore cell heterogeneity, cell behavior is a rich phenotypic space that may allow for identification of relevant cell states. Inference of cell state from cell behavior across a time course may enable the investigation of dynamics of transitions between heterogeneous cell states, a task difficult to perform with destructive molecular observations. Cell motility is one such easily observed cell behavior with known biomedical relevance. To investigate heterogenous cell states and their dynamics through the lens of cell behavior, we developed Heteromotility, a software tool to extract quantitative motility features from timelapse cell images. In mouse embryonic fibroblasts (MEFs), myoblasts, and muscle stem cells (MuSCs), Heteromotility analysis identifies multiple motility phenotypes within the population. In all three systems, the motility state identity of individual cells is dynamic. Quantification of state transitions reveals that MuSCs undergoing activation transition through progressive motility states toward the myoblast phenotype. Transition rates during MuSC activation suggest non-linear kinetics. By probability flux analysis, we find that this MuSC motility state system breaks detailed balance, while the MEF and myoblast systems do not. Balanced behavior state transitions can be captured by equilibrium formalisms, while unbalanced switching between states violates equilibrium conditions and would require an external driving force. Our data indicate that the system regulating cell behavior can be decomposed into a set of attractor states which depend on the identity of the cell, together with a set of transitions between states. These results support a conceptual view of cell populations as dynamical systems, responding to inputs from signaling pathways and generating outputs in the form of state transitions and observable motile behaviors.
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Affiliation(s)
- Jacob C. Kimmel
- Dept. of Biochemistry and Biophysics, Center for Cellular Construction, University of California San Francisco, San Francisco, CA, United States of America
- Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA, United States of America
| | - Amy Y. Chang
- Dept. of Biochemistry and Biophysics, Center for Cellular Construction, University of California San Francisco, San Francisco, CA, United States of America
| | - Andrew S. Brack
- Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA, United States of America
- Dept. of Orthopedic Surgery, University of California San Francisco, San Francisco, CA, United States of America
| | - Wallace F. Marshall
- Dept. of Biochemistry and Biophysics, Center for Cellular Construction, University of California San Francisco, San Francisco, CA, United States of America
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Masuzzo P, Huyck L, Simiczyjew A, Ampe C, Martens L, Van Troys M. An end-to-end software solution for the analysis of high-throughput single-cell migration data. Sci Rep 2017; 7:42383. [PMID: 28205527 PMCID: PMC5304333 DOI: 10.1038/srep42383] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Accepted: 01/10/2017] [Indexed: 12/12/2022] Open
Abstract
The systematic study of single-cell migration requires the availability of software for assisting data inspection, quality control and analysis. This is especially important for high-throughput experiments, where multiple biological conditions are tested in parallel. Although the field of cell migration can count on different computational tools for cell segmentation and tracking, downstream data visualization, parameter extraction and statistical analysis are still left to the user and are currently not possible within a single tool. This article presents a completely new module for the open-source, cross-platform CellMissy software for cell migration data management. This module is the first tool to focus specifically on single-cell migration data downstream of image processing. It allows fast comparison across all tested conditions, providing automated data visualization, assisted data filtering and quality control, extraction of various commonly used cell migration parameters, and non-parametric statistical analysis. Importantly, the module enables parameters computation both at the trajectory- and at the step-level. Moreover, this single-cell analysis module is complemented by a new data import module that accommodates multiwell plate data obtained from high-throughput experiments, and is easily extensible through a plugin architecture. In conclusion, the end-to-end software solution presented here tackles a key bioinformatics challenge in the cell migration field, assisting researchers in their high-throughput data processing.
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Affiliation(s)
- Paola Masuzzo
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.,Department of Biochemistry, Ghent University, Ghent, Belgium
| | - Lynn Huyck
- Department of Biochemistry, Ghent University, Ghent, Belgium
| | - Aleksandra Simiczyjew
- Department of Cell Pathology, Faculty of Biotechnology, University of Wroclaw, Wroclaw, Poland
| | - Christophe Ampe
- Department of Biochemistry, Ghent University, Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.,Department of Biochemistry, Ghent University, Ghent, Belgium
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An Overview of data science uses in bioimage informatics. Methods 2017; 115:110-118. [DOI: 10.1016/j.ymeth.2016.12.014] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 12/09/2016] [Accepted: 12/30/2016] [Indexed: 01/17/2023] Open
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Gordonov S, Hwang MK, Wells A, Gertler FB, Lauffenburger DA, Bathe M. Time series modeling of live-cell shape dynamics for image-based phenotypic profiling. Integr Biol (Camb) 2016; 8:73-90. [PMID: 26658688 PMCID: PMC5058786 DOI: 10.1039/c5ib00283d] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Live-cell imaging can be used to capture spatio-temporal aspects of cellular responses that are not accessible to fixed-cell imaging. As the use of live-cell imaging continues to increase, new computational procedures are needed to characterize and classify the temporal dynamics of individual cells. For this purpose, here we present the general experimental-computational framework SAPHIRE (Stochastic Annotation of Phenotypic Individual-cell Responses) to characterize phenotypic cellular responses from time series imaging datasets. Hidden Markov modeling is used to infer and annotate morphological state and state-switching properties from image-derived cell shape measurements. Time series modeling is performed on each cell individually, making the approach broadly useful for analyzing asynchronous cell populations. Two-color fluorescent cells simultaneously expressing actin and nuclear reporters enabled us to profile temporal changes in cell shape following pharmacological inhibition of cytoskeleton-regulatory signaling pathways. Results are compared with existing approaches conventionally applied to fixed-cell imaging datasets, and indicate that time series modeling captures heterogeneous dynamic cellular responses that can improve drug classification and offer additional important insight into mechanisms of drug action. The software is available at http://saphire-hcs.org.
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Affiliation(s)
- Simon Gordonov
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- The David H. Koch Institute for Integrative Cancer Research, Cambridge, MA, USA
| | - Mun Kyung Hwang
- The David H. Koch Institute for Integrative Cancer Research, Cambridge, MA, USA
| | - Alan Wells
- Department of Pathology, University of Pittsburgh, and Pittsburgh VA Health System, Pittsburgh, PA, USA
| | - Frank B. Gertler
- The David H. Koch Institute for Integrative Cancer Research, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Douglas A. Lauffenburger
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- The David H. Koch Institute for Integrative Cancer Research, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Mark Bathe
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
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