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Shao Z, Buchanan LB, Zuanazzi D, Khan YN, Khan AR, Prodger JL. Comparison between a deep-learning and a pixel-based approach for the automated quantification of HIV target cells in foreskin tissue. Sci Rep 2024; 14:1985. [PMID: 38263439 PMCID: PMC10806185 DOI: 10.1038/s41598-024-52613-3] [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: 10/03/2023] [Accepted: 01/21/2024] [Indexed: 01/25/2024] Open
Abstract
The availability of target cells expressing the HIV receptors CD4 and CCR5 in genital tissue is a critical determinant of HIV susceptibility during sexual transmission. Quantification of immune cells in genital tissue is therefore an important outcome for studies on HIV susceptibility and prevention. Immunofluorescence microscopy allows for precise visualization of immune cells in mucosal tissues; however, this technique is limited in clinical studies by the lack of an accurate, unbiased, high-throughput image analysis method. Current pixel-based thresholding methods for cell counting struggle in tissue regions with high cell density and autofluorescence, both of which are common features in genital tissue. We describe a deep-learning approach using the publicly available StarDist method to count cells in immunofluorescence microscopy images of foreskin stained for nuclei, CD3, CD4, and CCR5. The accuracy of the model was comparable to manual counting (gold standard) and surpassed the capability of a previously described pixel-based cell counting method. We show that the performance of our deep-learning model is robust in tissue regions with high cell density and high autofluorescence. Moreover, we show that this deep-learning analysis method is both easy to implement and to adapt for the identification of other cell types in genital mucosal tissue.
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Affiliation(s)
- Zhongtian Shao
- Department of Microbiology and Immunology, The University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada
| | - Lane B Buchanan
- Department of Microbiology and Immunology, The University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada
| | - David Zuanazzi
- Department of Microbiology and Immunology, The University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada
| | - Yazan N Khan
- Department of Microbiology and Immunology, The University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada
| | - Ali R Khan
- Department of Medical Biophysics, The University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada
| | - Jessica L Prodger
- Department of Microbiology and Immunology, The University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada.
- Department of Epidemiology and Biostatistics, The University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada.
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2
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Jashnsaz H, Neuert G. Phenotypic consequences of logarithmic signaling in MAPK stress response. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.05.570188. [PMID: 38106069 PMCID: PMC10723343 DOI: 10.1101/2023.12.05.570188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
How cells respond to dynamic environmental changes is crucial for understanding fundamental biological processes and cell physiology. In this study, we developed an experimental and quantitative analytical framework to explore how dynamic stress gradients that change over time regulate cellular volume, signaling activation, and growth phenotypes. Our findings reveal that gradual stress conditions substantially enhance cell growth compared to conventional acute stress. This growth advantage correlates with a minimal reduction in cell volume dependent on the dynamic of stress. We explain the growth phenotype with our finding of a logarithmic signal transduction mechanism in the yeast Mitogen-Activated Protein Kinase (MAPK) osmotic stress response pathway. These insights into the interplay between gradual environments, cell volume change, dynamic cell signaling, and growth, advance our understanding of fundamental cellular processes in gradual stress environments.
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Affiliation(s)
- Hossein Jashnsaz
- Department of Molecular Physiology and Biophysics, School of Medicine, Vanderbilt University, Nashville, TN 37232 USA
| | - Gregor Neuert
- Department of Molecular Physiology and Biophysics, School of Medicine, Vanderbilt University, Nashville, TN 37232 USA
- Lead Contact
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3
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Aldughayfiq B, Ashfaq F, Jhanjhi NZ, Humayun M. YOLOv5-FPN: A Robust Framework for Multi-Sized Cell Counting in Fluorescence Images. Diagnostics (Basel) 2023; 13:2280. [PMID: 37443674 DOI: 10.3390/diagnostics13132280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 06/02/2023] [Accepted: 06/11/2023] [Indexed: 07/15/2023] Open
Abstract
Cell counting in fluorescence microscopy is an essential task in biomedical research for analyzing cellular dynamics and studying disease progression. Traditional methods for cell counting involve manual counting or threshold-based segmentation, which are time-consuming and prone to human error. Recently, deep learning-based object detection methods have shown promising results in automating cell counting tasks. However, the existing methods mainly focus on segmentation-based techniques that require a large amount of labeled data and extensive computational resources. In this paper, we propose a novel approach to detect and count multiple-size cells in a fluorescence image slide using You Only Look Once version 5 (YOLOv5) with a feature pyramid network (FPN). Our proposed method can efficiently detect multiple cells with different sizes in a single image, eliminating the need for pixel-level segmentation. We show that our method outperforms state-of-the-art segmentation-based approaches in terms of accuracy and computational efficiency. The experimental results on publicly available datasets demonstrate that our proposed approach achieves an average precision of 0.8 and a processing time of 43.9 ms per image. Our approach addresses the research gap in the literature by providing a more efficient and accurate method for cell counting in fluorescence microscopy that requires less computational resources and labeled data.
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Affiliation(s)
- Bader Aldughayfiq
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia
| | - Farzeen Ashfaq
- School of Computer Science (SCS), Taylor's University, Subang Jaya 47500, Malaysia
| | - N Z Jhanjhi
- School of Computer Science (SCS), Taylor's University, Subang Jaya 47500, Malaysia
| | - Mamoona Humayun
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia
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4
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Vo HD, Forero-Quintero LS, Aguilera LU, Munsky B. Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise. Front Cell Dev Biol 2023; 11:1133994. [PMID: 37305680 PMCID: PMC10250612 DOI: 10.3389/fcell.2023.1133994] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 05/10/2023] [Indexed: 06/13/2023] Open
Abstract
Introduction: Despite continued technological improvements, measurement errors always reduce or distort the information that any real experiment can provide to quantify cellular dynamics. This problem is particularly serious for cell signaling studies to quantify heterogeneity in single-cell gene regulation, where important RNA and protein copy numbers are themselves subject to the inherently random fluctuations of biochemical reactions. Until now, it has not been clear how measurement noise should be managed in addition to other experiment design variables (e.g., sampling size, measurement times, or perturbation levels) to ensure that collected data will provide useful insights on signaling or gene expression mechanisms of interest. Methods: We propose a computational framework that takes explicit consideration of measurement errors to analyze single-cell observations, and we derive Fisher Information Matrix (FIM)-based criteria to quantify the information value of distorted experiments. Results and Discussion: We apply this framework to analyze multiple models in the context of simulated and experimental single-cell data for a reporter gene controlled by an HIV promoter. We show that the proposed approach quantitatively predicts how different types of measurement distortions affect the accuracy and precision of model identification, and we demonstrate that the effects of these distortions can be mitigated through explicit consideration during model inference. We conclude that this reformulation of the FIM could be used effectively to design single-cell experiments to optimally harvest fluctuation information while mitigating the effects of image distortion.
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Affiliation(s)
- Huy D. Vo
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO, United States
| | - Linda S. Forero-Quintero
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO, United States
| | - Luis U. Aguilera
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO, United States
| | - Brian Munsky
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO, United States
- School of Biomedical Engineering, Colorado State University, Fort Collins, CO, United States
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5
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Randall RS, Jourdain C, Nowicka A, Kaduchová K, Kubová M, Ayoub MA, Schubert V, Tatout C, Colas I, Kalyanikrishna, Desset S, Mermet S, Boulaflous-Stevens A, Kubalová I, Mandáková T, Heckmann S, Lysak MA, Panatta M, Santoro R, Schubert D, Pecinka A, Routh D, Baroux C. Image analysis workflows to reveal the spatial organization of cell nuclei and chromosomes. Nucleus 2022; 13:277-299. [PMID: 36447428 PMCID: PMC9754023 DOI: 10.1080/19491034.2022.2144013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Nucleus, chromatin, and chromosome organization studies heavily rely on fluorescence microscopy imaging to elucidate the distribution and abundance of structural and regulatory components. Three-dimensional (3D) image stacks are a source of quantitative data on signal intensity level and distribution and on the type and shape of distribution patterns in space. Their analysis can lead to novel insights that are otherwise missed in qualitative-only analyses. Quantitative image analysis requires specific software and workflows for image rendering, processing, segmentation, setting measurement points and reference frames and exporting target data before further numerical processing and plotting. These tasks often call for the development of customized computational scripts and require an expertise that is not broadly available to the community of experimental biologists. Yet, the increasing accessibility of high- and super-resolution imaging methods fuels the demand for user-friendly image analysis workflows. Here, we provide a compendium of strategies developed by participants of a training school from the COST action INDEPTH to analyze the spatial distribution of nuclear and chromosomal signals from 3D image stacks, acquired by diffraction-limited confocal microscopy and super-resolution microscopy methods (SIM and STED). While the examples make use of one specific commercial software package, the workflows can easily be adapted to concurrent commercial and open-source software. The aim is to encourage biologists lacking custom-script-based expertise to venture into quantitative image analysis and to better exploit the discovery potential of their images.Abbreviations: 3D FISH: three-dimensional fluorescence in situ hybridization; 3D: three-dimensional; ASY1: ASYNAPTIC 1; CC: chromocenters; CO: Crossover; DAPI: 4',6-diamidino-2-phenylindole; DMC1: DNA MEIOTIC RECOMBINASE 1; DSB: Double-Strand Break; FISH: fluorescence in situ hybridization; GFP: GREEN FLUORESCENT PROTEIN; HEI10: HUMAN ENHANCER OF INVASION 10; NCO: Non-Crossover; NE: Nuclear Envelope; Oligo-FISH: oligonucleotide fluorescence in situ hybridization; RNPII: RNA Polymerase II; SC: Synaptonemal Complex; SIM: structured illumination microscopy; ZMM (ZIP: MSH4: MSH5 and MER3 proteins); ZYP1: ZIPPER-LIKE PROTEIN 1.
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Affiliation(s)
- Ricardo S Randall
- Department of Plant and Microbial Biology, Zürich-Basel Plant Science Center, University of Zürich, Zürich, Switzerland
| | | | - Anna Nowicka
- Centre of the Region Haná for Biotechnological and Agricultural Research (CRH), Institute of Experimental Botany, v. v. i. (IEB), Olomouc, Czech Republic
| | - Kateřina Kaduchová
- Centre of the Region Haná for Biotechnological and Agricultural Research (CRH), Institute of Experimental Botany, v. v. i. (IEB), Olomouc, Czech Republic
| | - Michaela Kubová
- Central European Institute of Technology (CEITEC) and Department of Experimental Biology, Masaryk University, Brno, Czech Republic
| | - Mohammad A. Ayoub
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, D-06466Seeland, Germany
| | - Veit Schubert
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, D-06466Seeland, Germany
| | - Christophe Tatout
- Institut Génétique, Reproduction et Développement (GReD), Université Clermont Auvergne, CNRS, INSERM, 63001Clermont-Ferrand, France
| | - Isabelle Colas
- The James Hutton Institute, Errol Road, Invergowrie, DD2 5DA, Scotland UK
| | | | - Sophie Desset
- Institut Génétique, Reproduction et Développement (GReD), Université Clermont Auvergne, CNRS, INSERM, 63001Clermont-Ferrand, France
| | - Sarah Mermet
- Institut Génétique, Reproduction et Développement (GReD), Université Clermont Auvergne, CNRS, INSERM, 63001Clermont-Ferrand, France
| | - Aurélia Boulaflous-Stevens
- Institut Génétique, Reproduction et Développement (GReD), Université Clermont Auvergne, CNRS, INSERM, 63001Clermont-Ferrand, France
| | - Ivona Kubalová
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, D-06466Seeland, Germany
| | - Terezie Mandáková
- Central European Institute of Technology (CEITEC) and Department of Experimental Biology, Masaryk University, Brno, Czech Republic
| | - Stefan Heckmann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, D-06466Seeland, Germany
| | - Martin A. Lysak
- Central European Institute of Technology (CEITEC) and National Centre for Biomolecular Research, Masaryk University, Brno, Czech Republic
| | - Martina Panatta
- Department of Molecular Mechanisms of Disease, DMMD, University of Zürich, Zürich, Switzerland
| | - Raffaella Santoro
- Department of Molecular Mechanisms of Disease, DMMD, University of Zürich, Zürich, Switzerland
| | | | - Ales Pecinka
- Centre of the Region Haná for Biotechnological and Agricultural Research (CRH), Institute of Experimental Botany, v. v. i. (IEB), Olomouc, Czech Republic
| | - Devin Routh
- Service and Support for Science IT (S3IT), Universität Zürich, Zürich, Switzerland
| | - Célia Baroux
- Department of Plant and Microbial Biology, Zürich-Basel Plant Science Center, University of Zürich, Zürich, Switzerland,CONTACT Célia Baroux Department of Plant and Microbial Biology, Zürich-Basel Plant Science Center, University of Zürich, Zürich, Switzerland
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6
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Morris TA, Eldeen S, Tran RDH, Grosberg A. A comprehensive review of computational and image analysis techniques for quantitative evaluation of striated muscle tissue architecture. BIOPHYSICS REVIEWS 2022; 3:041302. [PMID: 36407035 PMCID: PMC9667907 DOI: 10.1063/5.0057434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 10/03/2022] [Indexed: 06/16/2023]
Abstract
Unbiased evaluation of morphology is crucial to understanding development, mechanics, and pathology of striated muscle tissues. Indeed, the ability of striated muscles to contract and the strength of their contraction is dependent on their tissue-, cellular-, and cytoskeletal-level organization. Accordingly, the study of striated muscles often requires imaging and assessing aspects of their architecture at multiple different spatial scales. While an expert may be able to qualitatively appraise tissues, it is imperative to have robust, repeatable tools to quantify striated myocyte morphology and behavior that can be used to compare across different labs and experiments. There has been a recent effort to define the criteria used by experts to evaluate striated myocyte architecture. In this review, we will describe metrics that have been developed to summarize distinct aspects of striated muscle architecture in multiple different tissues, imaged with various modalities. Additionally, we will provide an overview of metrics and image processing software that needs to be developed. Importantly to any lab working on striated muscle platforms, characterization of striated myocyte morphology using the image processing pipelines discussed in this review can be used to quantitatively evaluate striated muscle tissues and contribute to a robust understanding of the development and mechanics of striated muscles.
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Affiliation(s)
| | - Sarah Eldeen
- Center for Complex Biological Systems, University of California, Irvine, California 92697-2700, USA
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7
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Ye G, Kaya M. Automated Cell Foreground–Background Segmentation with Phase-Contrast Microscopy Images: An Alternative to Machine Learning Segmentation Methods with Small-Scale Data. Bioengineering (Basel) 2022; 9:bioengineering9020081. [PMID: 35200434 PMCID: PMC8869246 DOI: 10.3390/bioengineering9020081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 02/12/2022] [Accepted: 02/15/2022] [Indexed: 12/04/2022] Open
Abstract
Cell segmentation is a critical step for image-based experimental analysis. Existing cell segmentation methods are neither entirely automated nor perform well under basic laboratory microscopy. This study proposes an efficient and automated cell segmentation method involving morphological operations to automatically achieve cell segmentation for phase-contrast microscopes. Manual/visual counting of cell segmentation serves as the control group (156 images as ground truth) to evaluate the proposed method’s performance. The proposed technology’s adaptive performance is assessed at varying conditions, including artificial blurriness, illumination, and image size. Compared to the Trainable Weka Segmentation method, the Empirical Gradient Threshold method, and the ilastik segmentation software, the proposed method achieved better segmentation accuracy (dice coefficient: 90.07, IoU: 82.16%, and 6.51% as the average relative error on measuring cell area). The proposed method also has good reliability, even under unfavored imaging conditions at which manual labeling or human intervention is inefficient. Additionally, similar degrees of segmentation accuracy were confirmed when the ground truth data and the generated data from the proposed method were applied individually to train modified U-Net models (16848 images). These results demonstrated good accuracy and high practicality of the proposed cell segmentation method with phase-contrast microscopy image data.
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8
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Vizcarra JC, Burlingame EA, Hug CB, Goltsev Y, White BS, Tyson DR, Sokolov A. A community-based approach to image analysis of cells, tissues and tumors. Comput Med Imaging Graph 2022; 95:102013. [PMID: 34864359 PMCID: PMC8761177 DOI: 10.1016/j.compmedimag.2021.102013] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 11/09/2021] [Accepted: 11/09/2021] [Indexed: 01/03/2023]
Abstract
Emerging multiplexed imaging platforms provide an unprecedented view of an increasing number of molecular markers at subcellular resolution and the dynamic evolution of tumor cellular composition. As such, they are capable of elucidating cell-to-cell interactions within the tumor microenvironment that impact clinical outcome and therapeutic response. However, the rapid development of these platforms has far outpaced the computational methods for processing and analyzing the data they generate. While being technologically disparate, all imaging assays share many computational requirements for post-collection data processing. As such, our Image Analysis Working Group (IAWG), composed of researchers in the Cancer Systems Biology Consortium (CSBC) and the Physical Sciences - Oncology Network (PS-ON), convened a workshop on "Computational Challenges Shared by Diverse Imaging Platforms" to characterize these common issues and a follow-up hackathon to implement solutions for a selected subset of them. Here, we delineate these areas that reflect major axes of research within the field, including image registration, segmentation of cells and subcellular structures, and identification of cell types from their morphology. We further describe the logistical organization of these events, believing our lessons learned can aid others in uniting the imaging community around self-identified topics of mutual interest, in designing and implementing operational procedures to address those topics and in mitigating issues inherent in image analysis (e.g., sharing exemplar images of large datasets and disseminating baseline solutions to hackathon challenges through open-source code repositories).
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Affiliation(s)
- Juan Carlos Vizcarra
- Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA, USA
| | - Erik A Burlingame
- Computational Biology Program, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Clemens B Hug
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Boston, MA, USA
| | - Yury Goltsev
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | - Brian S White
- Computational Oncology, Sage Bionetworks, Seattle, WA, USA
| | - Darren R Tyson
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Artem Sokolov
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Boston, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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9
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Jashnsaz H, Fox ZR, Munsky B, Neuert G. Building predictive signaling models by perturbing yeast cells with time-varying stimulations resulting in distinct signaling responses. STAR Protoc 2021; 2:100660. [PMID: 34286292 PMCID: PMC8273411 DOI: 10.1016/j.xpro.2021.100660] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This protocol provides a step-by-step approach to perturb single cells with time-varying stimulation profiles, collect distinct signaling responses, and use these to infer a system of ordinary differential equations to capture and predict dynamics of protein-protein regulation in signal transduction pathways. The models are validated by predicting the signaling activation upon new cell stimulation conditions. In comparison to using standard step-like stimulations, application of diverse time-varying cell stimulations results in better inference of model parameters and substantially improves model predictions. For complete details on the use and results of this protocol, please refer to Jashnsaz et al. (2020). Diverse time-varying cell stimulations result in distinct signaling activation dynamics Signaling models fit step stimuli responses well but result in poor predictions Distinct responses upon diverse time-varying stimulations improve model predictions Temporal stimulation of pathways result in novel signaling dynamics and mechanisms
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Affiliation(s)
- Hossein Jashnsaz
- Department of Molecular Physiology and Biophysics, School of Medicine, Vanderbilt University, Nashville, TN 37232 USA
| | - Zachary R Fox
- Inria Paris, Paris 75012, France.,Institut Pasteur, USR 3756 IP CNRS, Paris 75015, France.,Keck Scholars, School of Biomedical Engineering, Colorado State University, Fort Collins, CO 80523 USA
| | - Brian Munsky
- Keck Scholars, School of Biomedical Engineering, Colorado State University, Fort Collins, CO 80523 USA.,Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO 80523 USA
| | - Gregor Neuert
- Department of Molecular Physiology and Biophysics, School of Medicine, Vanderbilt University, Nashville, TN 37232 USA.,Department of Biomedical Engineering, School of Engineering, Vanderbilt University, Nashville, TN 37232 USA.,Department of Pharmacology, School of Medicine, Vanderbilt University, Nashville, TN 37232 USA
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10
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Jeong EJ, Choi D, Lee DW. U-Net Deep-Learning-Based 3D Cell Counter for the Quality Control of 3D Cell-Based Assays through Seed Cell Measurement. SLAS Technol 2021; 26:468-476. [PMID: 34078148 DOI: 10.1177/24726303211017532] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Conventional cell-counting software uses contour or watershed segmentations and focuses on identifying two-dimensional (2D) cells attached on the bottom of plastic plates. Recently developed software has been useful tools for the quality control of 2D cell-based assays by measuring initial seed cell numbers. These algorithms do not, however, quantitatively test in three-dimensional (3D) cell-based assays using extracellular matrix (ECM), because cells are aggregated and overlapped in the 3D structure of the ECM such as Matrigel, collagen, and alginate. Such overlapped and aggregated cells make it difficult to segment cells and to count the number of cells accurately. It is important, however, to determine the number of cells to standardize experiments and ensure the reproducibility of 3D cell-based assays. In this study, we apply a 3D cell-counting method using U-net deep learning to high-density aggregated cells in ECM to identify initial seed cell numbers. The proposed method showed a 10% counting error in high-density aggregated cells, while the contour and watershed segmentations showed 30% and 40% counting errors, respectively. Thus, the proposed method can reduce the seed cell-counting error in 3D cell-based assays by providing the exact number of cells to researchers, thereby enabling the acquisition of quality control in 3D cell-based assays.
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Affiliation(s)
- Eun Ji Jeong
- Department of Biomedical Engineering, Konyang University, Daejeon, Korea
| | - Donghyuk Choi
- Department of Biomedical Engineering, Konyang University, Daejeon, Korea
| | - Dong Woo Lee
- Department of Biomedical Engineering, Konyang University, Daejeon, Korea
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11
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Learning deep features for dead and living breast cancer cell classification without staining. Sci Rep 2021; 11:10304. [PMID: 33986434 PMCID: PMC8119670 DOI: 10.1038/s41598-021-89895-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 05/04/2021] [Indexed: 12/17/2022] Open
Abstract
Automated cell classification in cancer biology is a challenging topic in computer vision and machine learning research. Breast cancer is the most common malignancy in women that usually involves phenotypically diverse populations of breast cancer cells and an heterogeneous stroma. In recent years, automated microscopy technologies are allowing the study of live cells over extended periods of time, simplifying the task of compiling large image databases. For instance, there have been several studies oriented towards building machine learning systems capable of automatically classifying images of different cell types (i.e. motor neurons, stem cells). In this work we were interested in classifying breast cancer cells as live or dead, based on a set of automatically retrieved morphological characteristics using image processing techniques. Our hypothesis is that live-dead classification can be performed without any staining and using only bright-field images as input. We tackled this problem using the JIMT-1 breast cancer cell line that grows as an adherent monolayer. First, a vast image set composed by JIMT-1 human breast cancer cells that had been exposed to a chemotherapeutic drug treatment (doxorubicin and paclitaxel) or vehicle control was compiled. Next, several classifiers were trained based on well-known convolutional neural networks (CNN) backbones to perform supervised classification using labels obtained from fluorescence microscopy images associated with each bright-field image. Model performances were evaluated and compared on a large number of bright-field images. The best model reached an AUC = 0.941 for classifying breast cancer cells without treatment. Furthermore, it reached AUC = 0.978 when classifying breast cancer cells under drug treatment. Our results highlight the potential of machine learning and computational image analysis to build new diagnosis tools that benefit the biomedical field by reducing cost, time, and stimulating work reproducibility. More importantly, we analyzed the way our classifiers clusterize bright-field images in the learned high-dimensional embedding and linked these groups to salient visual characteristics in live-dead cell biology observed by trained experts.
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12
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A rate threshold mechanism regulates MAPK stress signaling and survival. Proc Natl Acad Sci U S A 2021; 118:2004998118. [PMID: 33443180 DOI: 10.1073/pnas.2004998118] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Cells are exposed to changes in extracellular stimulus concentration that vary as a function of rate. However, how cells integrate information conveyed from stimulation rate along with concentration remains poorly understood. Here, we examined how varying the rate of stress application alters budding yeast mitogen-activated protein kinase (MAPK) signaling and cell behavior at the single-cell level. We show that signaling depends on a rate threshold that operates in conjunction with stimulus concentration to determine the timing of MAPK signaling during rate-varying stimulus treatments. We also discovered that the stimulation rate threshold and stimulation rate-dependent cell survival are sensitive to changes in the expression levels of the Ptp2 phosphatase, but not of another phosphatase that similarly regulates osmostress signaling during switch-like treatments. Our results demonstrate that stimulation rate is a regulated determinant of cell behavior and provide a paradigm to guide the dissection of major stimulation rate dependent mechanisms in other systems.
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13
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Bingham GC, Lee F, Naba A, Barker TH. Spatial-omics: Novel approaches to probe cell heterogeneity and extracellular matrix biology. Matrix Biol 2020; 91-92:152-166. [DOI: 10.1016/j.matbio.2020.04.004] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 04/22/2020] [Accepted: 04/23/2020] [Indexed: 12/12/2022]
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Piccinini F, Balassa T, Carbonaro A, Diosdi A, Toth T, Moshkov N, Tasnadi EA, Horvath P. Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregates. Comput Struct Biotechnol J 2020; 18:1287-1300. [PMID: 32612752 PMCID: PMC7303562 DOI: 10.1016/j.csbj.2020.05.022] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 05/22/2020] [Accepted: 05/23/2020] [Indexed: 12/25/2022] Open
Abstract
Today, we are fully immersed into the era of 3D biology. It has been extensively demonstrated that 3D models: (a) better mimic the physiology of human tissues; (b) can effectively replace animal models; (c) often provide more reliable results than 2D ones. Accordingly, anti-cancer drug screenings and toxicology studies based on multicellular 3D biological models, the so-called "-oids" (e.g. spheroids, tumoroids, organoids), are blooming in the literature. However, the complex nature of these systems limit the manual quantitative analyses of single cells' behaviour in the culture. Accordingly, the demand for advanced software tools that are able to perform phenotypic analysis is fundamental. In this work, we describe the freely accessible tools that are currently available for biologists and researchers interested in analysing the effects of drugs/treatments on 3D multicellular -oids at a single-cell resolution level. In addition, using publicly available nuclear stained datasets we quantitatively compare the segmentation performance of 9 specific tools.
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Affiliation(s)
- Filippo Piccinini
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Cancer Research Hospital, Meldola, FC, Italy
| | - Tamas Balassa
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
| | - Antonella Carbonaro
- Department of Computer Science and Engineering, University of Bologna, Italy
| | - Akos Diosdi
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Doctoral School of Biology, University of Szeged, Hungary
| | - Timea Toth
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Doctoral School of Biology, University of Szeged, Hungary
| | - Nikita Moshkov
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Doctoral School of Interdisciplinary Medicine, University of Szeged, Hungary
- National Research University Higher School of Economics, Moscow, Russia
| | - Ervin A. Tasnadi
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Doctoral School of Computer Science, University of Szeged, Hungary
| | - Peter Horvath
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Single-Cell Technologies Ltd., Szeged, Hungary
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Abstract
BACKGROUND Cell nuclei segmentation is a fundamental task in microscopy image analysis, based on which multiple biological related analysis can be performed. Although deep learning (DL) based techniques have achieved state-of-the-art performances in image segmentation tasks, these methods are usually complex and require support of powerful computing resources. In addition, it is impractical to allocate advanced computing resources to each dark- or bright-field microscopy, which is widely employed in vast clinical institutions, considering the cost of medical exams. Thus, it is essential to develop accurate DL based segmentation algorithms working with resources-constraint computing. RESULTS An enhanced, light-weighted U-Net (called U-Net+) with modified encoded branch is proposed to potentially work with low-resources computing. Through strictly controlled experiments, the average IOU and precision of U-Net+ predictions are confirmed to outperform other prevalent competing methods with 1.0% to 3.0% gain on the first stage test set of 2018 Kaggle Data Science Bowl cell nuclei segmentation contest with shorter inference time. CONCLUSIONS Our results preliminarily demonstrate the potential of proposed U-Net+ in correctly spotting microscopy cell nuclei with resources-constraint computing.
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Affiliation(s)
- Feixiao Long
- Hudongfeng Technology (Beijing) Co., Ltd., Sanjianfang South No.4, DREAM 2049 B05, Chaoyang District, Beijing, China.
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