1
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Gao Z, Han K, Hua X, Liu W, Jia S. hydroSIM: super-resolution speckle illumination microscopy with a hydrogel diffuser. BIOMEDICAL OPTICS EXPRESS 2024; 15:3574-3585. [PMID: 38867780 PMCID: PMC11166422 DOI: 10.1364/boe.521521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 03/27/2024] [Accepted: 04/18/2024] [Indexed: 06/14/2024]
Abstract
Super-resolution microscopy has emerged as an indispensable methodology for probing the intricacies of cellular biology. Structured illumination microscopy (SIM), in particular, offers an advantageous balance of spatial and temporal resolution, allowing for visualizing cellular processes with minimal disruption to biological specimens. However, the broader adoption of SIM remains hampered by the complexity of instrumentation and alignment. Here, we introduce speckle-illumination super-resolution microscopy using hydrogel diffusers (hydroSIM). The study utilizes the high scattering and optical transmissive properties of hydrogel materials and realizes a remarkably simplified approach to plug-in super-resolution imaging via a common epi-fluorescence platform. We demonstrate the hydroSIM system using various phantom and biological samples, and the results exhibited effective 3D resolution doubling, optical sectioning, and high contrast. We foresee hydroSIM, a cost-effective, biocompatible, and user-accessible super-resolution methodology, to significantly advance a wide range of biomedical imaging and applications.
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Affiliation(s)
- Zijun Gao
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia 30332, USA
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
- School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Keyi Han
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia 30332, USA
| | - Xuanwen Hua
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia 30332, USA
| | - Wenhao Liu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia 30332, USA
| | - Shu Jia
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia 30332, USA
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
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2
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Li C, Xie SS, Wang J, Sharvia S, Chan KY. SC-Track: a robust cell-tracking algorithm for generating accurate single-cell lineages from diverse cell segmentations. Brief Bioinform 2024; 25:bbae192. [PMID: 38704671 PMCID: PMC11070058 DOI: 10.1093/bib/bbae192] [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: 01/12/2024] [Revised: 03/18/2024] [Accepted: 04/10/2024] [Indexed: 05/06/2024] Open
Abstract
Computational analysis of fluorescent timelapse microscopy images at the single-cell level is a powerful approach to study cellular changes that dictate important cell fate decisions. Core to this approach is the need to generate reliable cell segmentations and classifications necessary for accurate quantitative analysis. Deep learning-based convolutional neural networks (CNNs) have emerged as a promising solution to these challenges. However, current CNNs are prone to produce noisy cell segmentations and classifications, which is a significant barrier to constructing accurate single-cell lineages. To address this, we developed a novel algorithm called Single Cell Track (SC-Track), which employs a hierarchical probabilistic cache cascade model based on biological observations of cell division and movement dynamics. Our results show that SC-Track performs better than a panel of publicly available cell trackers on a diverse set of cell segmentation types. This cell-tracking performance was achieved without any parameter adjustments, making SC-Track an excellent generalized algorithm that can maintain robust cell-tracking performance in varying cell segmentation qualities, cell morphological appearances and imaging conditions. Furthermore, SC-Track is equipped with a cell class correction function to improve the accuracy of cell classifications in multiclass cell segmentation time series. These features together make SC-Track a robust cell-tracking algorithm that works well with noisy cell instance segmentation and classification predictions from CNNs to generate accurate single-cell lineages and classifications.
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Affiliation(s)
- Chengxin Li
- Department of Cardiovascular Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, P. R. China
- Centre for Cellular Biology and Signalling, Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Haining, 314400, P. R. China
| | - Shuang Shuang Xie
- Centre for Cellular Biology and Signalling, Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Haining, 314400, P. R. China
| | - Jiaqi Wang
- Centre for Cellular Biology and Signalling, Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Haining, 314400, P. R. China
| | - Septavera Sharvia
- Department of Computer Science, University of Hull, Hull, HU6 7RX, UK
| | - Kuan Yoow Chan
- Department of Cardiovascular Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, P. R. China
- Centre for Cellular Biology and Signalling, Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Haining, 314400, P. R. China
- College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, EH4 2XR, UK
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3
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Chen Q, Son J, Jia S. Implementation of miniaturized modular-array fluorescence microscopy for long-term live-cell imaging. APPLIED OPTICS 2023; 62:2456-2461. [PMID: 37132792 DOI: 10.1364/ao.483279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Fluorescence microscopy imaging of live cells has provided consistent monitoring of dynamic cellular activities and interactions. However, because current live-cell imaging systems are limited in their adaptability, portable cell imaging systems have been adapted by a variety of strategies, including miniaturized fluorescence microscopy. Here, we provide a protocol for the construction and operational process of miniaturized modular-array fluorescence microscopy (MAM). The MAM system is built in a portable size (15c m×15c m×3c m) and provides in situ cell imaging inside an incubator with a subcellular lateral resolution (∼3µm). We demonstrated the improved stability of the MAM system with fluorescent targets and live HeLa cells, enabling long-term imaging for 12 h without the need for external support or post-processing. We believe the protocol could guide scientists to construct a compact portable fluorescence imaging system and perform time-lapse in situ single-cell imaging and analysis.
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4
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Hughes FA, Barr AR, Thomas P. Patterns of interdivision time correlations reveal hidden cell cycle factors. eLife 2022; 11:e80927. [PMID: 36377847 PMCID: PMC9822260 DOI: 10.7554/elife.80927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 11/14/2022] [Indexed: 11/16/2022] Open
Abstract
The time taken for cells to complete a round of cell division is a stochastic process controlled, in part, by intracellular factors. These factors can be inherited across cellular generations which gives rise to, often non-intuitive, correlation patterns in cell cycle timing between cells of different family relationships on lineage trees. Here, we formulate a framework of hidden inherited factors affecting the cell cycle that unifies known cell cycle control models and reveals three distinct interdivision time correlation patterns: aperiodic, alternator, and oscillator. We use Bayesian inference with single-cell datasets of cell division in bacteria, mammalian and cancer cells, to identify the inheritance motifs that underlie these datasets. From our inference, we find that interdivision time correlation patterns do not identify a single cell cycle model but generally admit a broad posterior distribution of possible mechanisms. Despite this unidentifiability, we observe that the inferred patterns reveal interpretable inheritance dynamics and hidden rhythmicity of cell cycle factors. This reveals that cell cycle factors are commonly driven by circadian rhythms, but their period may differ in cancer. Our quantitative analysis thus reveals that correlation patterns are an emergent phenomenon that impact cell proliferation and these patterns may be altered in disease.
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Affiliation(s)
- Fern A Hughes
- Department of Mathematics, Imperial College LondonLondonUnited Kingdom
- MRC London Institute of Medical SciencesLondonUnited Kingdom
| | - Alexis R Barr
- MRC London Institute of Medical SciencesLondonUnited Kingdom
- Institute of Clinical Sciences, Imperial College LondonLondonUnited Kingdom
| | - Philipp Thomas
- Department of Mathematics, Imperial College LondonLondonUnited Kingdom
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5
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Long Q, Feng L, Li Y, Zuo T, Chang L, Zhang Z, Xu P. Time-resolved quantitative phosphoproteomics reveals cellular responses induced by caffeine and coumarin. Toxicol Appl Pharmacol 2022; 449:116115. [PMID: 35691368 DOI: 10.1016/j.taap.2022.116115] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 05/27/2022] [Accepted: 06/06/2022] [Indexed: 11/30/2022]
Abstract
Protein phosphorylation is a critical way that cells respond to external signals and environmental stresses. However, the patterns of cellular response to chemicals at different times were largely unknown. Here, we used quantitative phosphoproteomics to analyze the cellular response of kinases and signaling pathways, as well as pattern change of phosphorylated substrates in HepG2 cells that were exposed to caffeine and coumarin for 10 min and 24 h. Comparing the 10 min and 24 h groups, 33 kinases were co-responded and 32 signaling pathways were co-enriched in caffeine treated samples, while 48 kinases and 34 signaling pathways were co-identified in coumarin treated samples. Instead, the percentage of co-identified phosphorylated substrates only accounted for 4.31% and 9.57% between 10 min and 24 h in caffeine and coumarin treated samples, respectively. The results showed that specific chemical exposure led to a bunch of the same kinases and signaling pathways changed in HepG2 cells, while the phosphorylated substrates were different. In addition, it was found that insulin signaling pathway was significantly enriched by both the caffeine and coumarin treatment. The pattern changes in phosphorylation of protein substrates, kinases and signaling pathways with varied chemicals and different time course shed light on the potential mechanism of cellular responses to endless chemical stimulation.
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Affiliation(s)
- Qi Long
- School of Basic Medicine, Anhui Medical University, Hefei 230032, China; State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Lijie Feng
- School of Basic Medicine, Anhui Medical University, Hefei 230032, China
| | - Yuan Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China; School of Medicine, Guizhou University, Guiyang 550025, China
| | - Tao Zuo
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Lei Chang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Zhenpeng Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China.
| | - Ping Xu
- School of Basic Medicine, Anhui Medical University, Hefei 230032, China; State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China; School of Medicine, Guizhou University, Guiyang 550025, China; School of Public Health, China Medical University, Shenyang 110122, China; Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding 071002, China.
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6
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Tognetti M, Gabor A, Yang M, Cappelletti V, Windhager J, Rueda OM, Charmpi K, Esmaeilishirazifard E, Bruna A, de Souza N, Caldas C, Beyer A, Picotti P, Saez-Rodriguez J, Bodenmiller B. Deciphering the signaling network of breast cancer improves drug sensitivity prediction. Cell Syst 2021; 12:401-418.e12. [PMID: 33932331 DOI: 10.1016/j.cels.2021.04.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 12/16/2020] [Accepted: 04/07/2021] [Indexed: 02/06/2023]
Abstract
One goal of precision medicine is to tailor effective treatments to patients' specific molecular markers of disease. Here, we used mass cytometry to characterize the single-cell signaling landscapes of 62 breast cancer cell lines and five lines from healthy tissue. We quantified 34 markers in each cell line upon stimulation by the growth factor EGF in the presence or absence of five kinase inhibitors. These data-on more than 80 million single cells from 4,000 conditions-were used to fit mechanistic signaling network models that provide insight into how cancer cells process information. Our dynamic single-cell-based models accurately predicted drug sensitivity and identified genomic features associated with drug sensitivity, including a missense mutation in DDIT3 predictive of PI3K-inhibition sensitivity. We observed similar trends in genotype-drug sensitivity associations in patient-derived xenograft mouse models. This work provides proof of principle that patient-specific single-cell measurements and modeling could inform effective precision medicine strategies.
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Affiliation(s)
- Marco Tognetti
- Department of Quantitative Biomedicine, University of Zürich, 8057 Zurich, Switzerland; Institute of Molecular Life Sciences, University of Zürich, 8057 Zurich, Switzerland; Institute of Molecular Systems Biology, ETH Zürich, 8093 Zurich, Switzerland; Molecular Life Science PhD Program, Life Science Zürich Graduate School, ETH Zürich and University of Zürich, 8057 Zurich, Switzerland
| | - Attila Gabor
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University, 69117 Heidelberg, Germany; Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany
| | - Mi Yang
- Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany; Faculty of Biosciences, Heidelberg University, 69117 Heidelberg, Germany
| | | | - Jonas Windhager
- Department of Quantitative Biomedicine, University of Zürich, 8057 Zurich, Switzerland; Institute of Molecular Life Sciences, University of Zürich, 8057 Zurich, Switzerland; Systems Biology PhD Program, Life Science Zürich Graduate School, ETH Zürich and University of Zürich, 8093 Zürich, Switzerland
| | - Oscar M Rueda
- Department of Oncology and Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge CB2 0RE, UK
| | - Konstantina Charmpi
- Cologne Excellence Cluster Cellular Stress Response in Aging-Associated Diseases (CECAD), Medical Faculty and Faculty of Mathematics and Natural Sciences, University of Cologne, 50923 Cologne, Germany
| | - Elham Esmaeilishirazifard
- Department of Oncology and Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge CB2 0RE, UK; Bioscience, R&D Oncology, Astra Zeneca, Cancer Research UK Cambridge Institute, Cambridge CB2 0RE, UK
| | - Alejandra Bruna
- Department of Oncology and Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge CB2 0RE, UK
| | - Natalie de Souza
- Department of Quantitative Biomedicine, University of Zürich, 8057 Zurich, Switzerland; Institute of Molecular Systems Biology, ETH Zürich, 8093 Zurich, Switzerland
| | - Carlos Caldas
- Department of Oncology and Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge CB2 0RE, UK; Cambridge Breast Unit, NIHR Cambridge Biomedical Research Centre and Cambridge Experimental Cancer Medicine Centre at Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
| | - Andreas Beyer
- Cologne Excellence Cluster Cellular Stress Response in Aging-Associated Diseases (CECAD), Medical Faculty and Faculty of Mathematics and Natural Sciences, University of Cologne, 50923 Cologne, Germany; Center for Molecular Medicine (CMMC), University of Cologne, 50923 Cologne, Germany; Institute for Genetics, Faculty of Mathematics and Natural Sciences, University of Cologne, 50923 Cologne, Germany
| | - Paola Picotti
- Institute of Molecular Systems Biology, ETH Zürich, 8093 Zurich, Switzerland
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University, 69117 Heidelberg, Germany; Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany
| | - Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zürich, 8057 Zurich, Switzerland; Institute of Molecular Life Sciences, University of Zürich, 8057 Zurich, Switzerland.
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7
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Kolesová H, Olejníčková V, Kvasilová A, Gregorovičová M, Sedmera D. Tissue clearing and imaging methods for cardiovascular development. iScience 2021; 24:102387. [PMID: 33981974 PMCID: PMC8086021 DOI: 10.1016/j.isci.2021.102387] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Tissue imaging in 3D using visible light is limited and various clearing techniques were developed to increase imaging depth, but none provides universal solution for all tissues at all developmental stages. In this review, we focus on different tissue clearing methods for 3D imaging of heart and vasculature, based on chemical composition (solvent-based, simple immersion, hyperhydration, and hydrogel embedding techniques). We discuss in detail compatibility of various tissue clearing techniques with visualization methods: fluorescence preservation, immunohistochemistry, nuclear staining, and fluorescent dyes vascular perfusion. We also discuss myocardium visualization using autofluorescence, tissue shrinking, and expansion. Then we overview imaging methods used to study cardiovascular system and live imaging. We discuss heart and vessels segmentation methods and image analysis. The review covers the whole process of cardiovascular system 3D imaging, starting from tissue clearing and its compatibility with various visualization methods to the types of imaging methods and resulting image analysis.
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Affiliation(s)
- Hana Kolesová
- Institute of Anatomy, First Faculty of Medicine, Charles University, Prague, Czech Republic
- Institute of Physiology, Czech Academy of Science, Prague, Czech Republic
| | - Veronika Olejníčková
- Institute of Anatomy, First Faculty of Medicine, Charles University, Prague, Czech Republic
- Institute of Physiology, Czech Academy of Science, Prague, Czech Republic
| | - Alena Kvasilová
- Institute of Anatomy, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Martina Gregorovičová
- Institute of Anatomy, First Faculty of Medicine, Charles University, Prague, Czech Republic
- Institute of Physiology, Czech Academy of Science, Prague, Czech Republic
| | - David Sedmera
- Institute of Anatomy, First Faculty of Medicine, Charles University, Prague, Czech Republic
- Institute of Physiology, Czech Academy of Science, Prague, Czech Republic
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8
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Norris D, Yang P, Shin SY, Kearney AL, Kim HJ, Geddes T, Senior AM, Fazakerley DJ, Nguyen LK, James DE, Burchfield JG. Signaling Heterogeneity is Defined by Pathway Architecture and Intercellular Variability in Protein Expression. iScience 2021; 24:102118. [PMID: 33659881 PMCID: PMC7892930 DOI: 10.1016/j.isci.2021.102118] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 01/07/2021] [Accepted: 01/22/2021] [Indexed: 12/12/2022] Open
Abstract
Insulin's activation of PI3K/Akt signaling, stimulates glucose uptake by enhancing delivery of GLUT4 to the cell surface. Here we examined the origins of intercellular heterogeneity in insulin signaling. Akt activation alone accounted for ~25% of the variance in GLUT4, indicating that additional sources of variance exist. The Akt and GLUT4 responses were highly reproducible within the same cell, suggesting the variance is between cells (extrinsic) and not within cells (intrinsic). Generalized mechanistic models (supported by experimental observations) demonstrated that the correlation between the steady-state levels of two measured signaling processes decreases with increasing distance from each other and that intercellular variation in protein expression (as an example of extrinsic variance) is sufficient to account for the variance in and between Akt and GLUT4. Thus, the response of a population to insulin signaling is underpinned by considerable single-cell heterogeneity that is largely driven by variance in gene/protein expression between cells. Insulin signaling is heterogeneous between cells in the same population The temporal response of signaling components within a cell is highly reproducible Upstream responses (Akt) can only partially predict downstream response (GLUT4) Protein expression variance is a driver of intercellular signaling heterogeneity
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Affiliation(s)
- Dougall Norris
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Pengyi Yang
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.,Computational Systems Biology Group, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
| | - Sung-Young Shin
- Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Monash University, Clayton, VIC 3800, Australia.,Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Alison L Kearney
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Hani Jieun Kim
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.,Computational Systems Biology Group, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
| | - Thomas Geddes
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.,Computational Systems Biology Group, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
| | - Alistair M Senior
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Daniel J Fazakerley
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia
| | - Lan K Nguyen
- Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Monash University, Clayton, VIC 3800, Australia.,Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - David E James
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia.,Sydney Medical School, The University of Sydney, Sydney, NSW 2006, Australia
| | - James G Burchfield
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
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9
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Son J, Mandracchia B, Jia S. Miniaturized modular-array fluorescence microscopy. BIOMEDICAL OPTICS EXPRESS 2020; 11:7221-7235. [PMID: 33408992 PMCID: PMC7747904 DOI: 10.1364/boe.410605] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 11/10/2020] [Accepted: 11/12/2020] [Indexed: 05/20/2023]
Abstract
Fluorescence live-cell imaging allows for continuous interrogation of cellular behaviors, and the recent development of portable live-cell imaging platforms has rapidly transformed conventional schemes with high adaptability, cost-effective functionalities and easy accessibility to cell-based assays. However, broader applications remain restrictive due to compatibility with conventional cell culture workflow and biochemical sensors, accessibility to up-right physiological imaging, or parallelization of data acquisition. Here, we introduce miniaturized modular-array fluorescence microscopy (MAM) for compact live-cell imaging in flexible formats. We advance the current miniscopy technology to devise an up-right modular architecture, each combining a gradient-index (GRIN) objective and individually-addressed illumination and acquisition components. Parallelization of an array of such modular devices allows for multi-site data acquisition in situ using conventional off-the-shelf cell chambers. Compared with existing methods, the device offers a high fluorescence sensitivity and efficiency, exquisite spatiotemporal resolution (∼3 µm and up to 60 Hz), a configuration compatible with conventional cell culture assays and physiological imaging, and an effective parallelization of data acquisition. The system has been demonstrated using various calibration and biological samples and experimental conditions, representing a promising solution to time-lapse in situ single-cell imaging and analysis.
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10
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Anti-BAFF-R antibody VAY-736 demonstrates promising preclinical activity in CLL and enhances effectiveness of ibrutinib. Blood Adv 2020; 3:447-460. [PMID: 30737226 DOI: 10.1182/bloodadvances.2018025684] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Accepted: 01/02/2019] [Indexed: 12/11/2022] Open
Abstract
The Bruton tyrosine kinase inhibitor (BTKi) ibrutinib has transformed chronic lymphocytic leukemia (CLL) therapy but requires continuous administration. These factors have spurred interest in combination treatments. Unlike with chemotherapy, CD20-directed antibody therapy has not improved the outcome of BTKi treatment. Whereas CD20 antigen density on CLL cells decreases during ibrutinib treatment, the B-cell activating factor (BAFF) and its receptor (BAFF-R) remain elevated. Furthermore, BAFF signaling via noncanonical NF-κB remains elevated with BTKi treatment. Blocking BAFF interaction with BAFF-R by using VAY-736, a humanized defucosylated engineered antibody directed against BAFF-R, antagonized BAFF-mediated apoptosis protection and signaling at the population and single-cell levels in CLL cells. Furthermore, VAY-736 showed superior antibody-dependent cellular cytotoxicity compared with CD20- and CD52-directed antibodies used in CLL. VAY-736 exhibited in vivo activity as a monotherapy and, when combined with ibrutinib, produced prolonged survival compared with either therapy alone. The in vivo activity of VAY-736 is dependent upon immunoreceptor tyrosine-based activation motif (ITAM)-mediated activation of effector cells as shown by using an ITAM-deficient mouse model. Collectively, our findings support targeting the BAFF signaling pathway with VAY-736 to more effectively treat CLL as a single agent and in combination with ibrutinib.
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11
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Andrei L, Kasas S, Ochoa Garrido I, Stanković T, Suárez Korsnes M, Vaclavikova R, Assaraf YG, Pešić M. Advanced technological tools to study multidrug resistance in cancer. Drug Resist Updat 2020; 48:100658. [DOI: 10.1016/j.drup.2019.100658] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 09/26/2019] [Accepted: 09/27/2019] [Indexed: 02/06/2023]
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12
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Qin Q, Laub S, Shi Y, Ouyang M, Peng Q, Zhang J, Wang Y, Lu S. Fluocell for Ratiometric and High-Throughput Live-Cell Image Visualization and Quantitation. FRONTIERS IN PHYSICS 2019; 7:154. [PMID: 33163483 PMCID: PMC7646842 DOI: 10.3389/fphy.2019.00154] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Spatiotemporal regulation of molecular activities dictates cellular function and fate. Investigation of dynamic molecular activities in live cells often requires the visualization and quantitation of fluorescent ratio image sequences with subcellular resolution and in high throughput. Hence, there is a great need for convenient software tools specifically designed with these capabilities. Here we describe a well-characterized open-source software package, Fluocell, customized to visualize pixelwise ratiometric images and calculate ratio time courses with subcellular resolution and in high throughput. Fluocell also provides group statistics and kinetic analysis functions for the quantified time courses, as well as 3D structure and function visualization for ratio images. The application of Fluocell is demonstrated by the ratiometric analysis of intensity images for several single-chain Förster (or fluorescence) resonance energy transfer (FRET)-based biosensors, allowing efficient quantification of dynamic molecular activities in a heterogeneous population of single live cells. Our analysis revealed distinct activation kinetics of Fyn kinase in the cytosolic and membrane compartments, and visualized a 4D spatiotemporal distribution of epigenetic signals in mitotic cells. Therefore, Fluocell provides an integrated environment for ratiometric live-cell image visualization and analysis, which generates high-quality single-cell dynamic data and allows the quantitative machine-learning of biophysical and biochemical computational models for molecular regulations in cells and tissues.
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Affiliation(s)
- Qin Qin
- Department of Bioengineering, Institute of Engineering in Medicine, University of California, San Diego, San Diego, CA, United States
| | - Shannon Laub
- Department of Bioengineering, Institute of Engineering in Medicine, University of California, San Diego, San Diego, CA, United States
| | - Yiwen Shi
- Department of Mathematics, Center of Computational Mathematics, University of California, San Diego, San Diego, CA, United State
| | - Mingxing Ouyang
- Institute of Biomedical Engineering and Health Sciences, Changzhou University, Changzhou, China
| | - Qin Peng
- Department of Bioengineering, Institute of Engineering in Medicine, University of California, San Diego, San Diego, CA, United States
| | - Jin Zhang
- Department of Pharmacology, University of California, San Diego, San Diego, CA, United States
| | - Yingxiao Wang
- Department of Bioengineering, Institute of Engineering in Medicine, University of California, San Diego, San Diego, CA, United States
| | - Shaoying Lu
- Department of Bioengineering, Institute of Engineering in Medicine, University of California, San Diego, San Diego, CA, United States
- Department of Mathematics, Center of Computational Mathematics, University of California, San Diego, San Diego, CA, United State
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13
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Wood NE, Doncic A. A fully-automated, robust, and versatile algorithm for long-term budding yeast segmentation and tracking. PLoS One 2019; 14:e0206395. [PMID: 30917124 PMCID: PMC6436761 DOI: 10.1371/journal.pone.0206395] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 03/08/2019] [Indexed: 12/17/2022] Open
Abstract
Live cell time-lapse microscopy, a widely-used technique to study gene expression and protein dynamics in single cells, relies on segmentation and tracking of individual cells for data generation. The potential of the data that can be extracted from this technique is limited by the inability to accurately segment a large number of cells from such microscopy images and track them over long periods of time. Existing segmentation and tracking algorithms either require additional dyes or markers specific to segmentation or they are highly specific to one imaging condition and cell morphology and/or necessitate manual correction. Here we introduce a fully automated, fast and robust segmentation and tracking algorithm for budding yeast that overcomes these limitations. Full automatization is achieved through a novel automated seeding method, which first generates coarse seeds, then automatically fine-tunes cell boundaries using these seeds and automatically corrects segmentation mistakes. Our algorithm can accurately segment and track individual yeast cells without any specific dye or biomarker. Moreover, we show how existing channels devoted to a biological process of interest can be used to improve the segmentation. The algorithm is versatile in that it accurately segments not only cycling cells with smooth elliptical shapes, but also cells with arbitrary morphologies (e.g. sporulating and pheromone treated cells). In addition, the algorithm is independent of the specific imaging method (bright-field/phase) and objective used (40X/63X/100X). We validate our algorithm's performance on 9 cases each entailing a different imaging condition, objective magnification and/or cell morphology. Taken together, our algorithm presents a powerful segmentation and tracking tool that can be adapted to numerous budding yeast single-cell studies.
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Affiliation(s)
- N. Ezgi Wood
- Department of Cell Biology, UT Southwestern Medical Center, Dallas, Texas, United States of America
- * E-mail:
| | - Andreas Doncic
- Department of Cell Biology, UT Southwestern Medical Center, Dallas, Texas, United States of America
- Green Center for Systems Biology, UT Southwestern Medical Center, Dallas, Texas, United States of America
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14
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A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics. Cell Syst 2019; 8:15-26.e11. [PMID: 30638813 DOI: 10.1016/j.cels.2018.12.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 10/16/2018] [Accepted: 12/11/2018] [Indexed: 01/26/2023]
Abstract
Single-cell time-lapse data provide the means for disentangling sources of cell-to-cell and intra-cellular variability, a key step for understanding heterogeneity in cell populations. However, single-cell analysis with dynamic models is a challenging open problem: current inference methods address only single-gene expression or neglect parameter correlations. We report on a simple, flexible, and scalable method for estimating cell-specific and population-average parameters of non-linear mixed-effects models of cellular networks, demonstrating its accuracy with a published model and dataset. We also propose sensitivity analysis for identifying which biological sub-processes quantitatively and dynamically contribute to cell-to-cell variability. Our application to endocytosis in yeast demonstrates that dynamic models of realistic size can be developed for the analysis of single-cell data and that shifting the focus from single reactions or parameters to nuanced and time-dependent contributions of sub-processes helps biological interpretation. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics.
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15
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Mouse and human HSPC immobilization in liquid culture by CD43- or CD44-antibody coating. Blood 2018; 131:1425-1429. [PMID: 29453290 DOI: 10.1182/blood-2017-07-794131] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Accepted: 01/13/2018] [Indexed: 12/26/2022] Open
Abstract
Keeping track of individual cell identifications is imperative to the study of dynamic single-cell behavior over time. Highly motile hematopoietic stem and progenitor cells (HSPCs) migrate quickly and do not adhere, and thus must be imaged very frequently to keep cell identifications. Even worse, they are also flushed away during medium exchange. To overcome these limitations, we tested antibody coating for reducing HSPC motility in vitro. Anti-CD43- and anti-CD44-antibody coating reduced the cell motility of mouse and human HSPCs in a concentration-dependent manner. This enables 2-dimensional (2D) colony formation without cell mixing in liquid cultures, massively increases time-lapse imaging throughput, and also maintains cell positions during media exchange. Anti-CD43 but not anti-CD44 coating reduces mouse HSPC proliferation with increasing concentrations. No relevant effects on cell survival or myeloid and megakaryocyte differentiation of hematopoietic stem cells and multipotent progenitors 1-5 were detected. Human umbilical cord hematopoietic CD34+ cell survival, proliferation, and differentiation were not affected by either coating. This approach both massively simplifies and accelerates continuous analysis of suspension cells, and enables the study of their behavior in dynamic rather than static culture conditions over time.
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16
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Marklein RA, Lam J, Guvendiren M, Sung KE, Bauer SR. Functionally-Relevant Morphological Profiling: A Tool to Assess Cellular Heterogeneity. Trends Biotechnol 2018; 36:105-118. [DOI: 10.1016/j.tibtech.2017.10.007] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Revised: 10/11/2017] [Accepted: 10/18/2017] [Indexed: 12/16/2022]
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17
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Reyes J, Lahav G. Leveraging and coping with uncertainty in the response of individual cells to therapy. Curr Opin Biotechnol 2017; 51:109-115. [PMID: 29288931 DOI: 10.1016/j.copbio.2017.12.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Accepted: 12/11/2017] [Indexed: 12/23/2022]
Abstract
Non-genetic heterogeneity fluctuates over diverse timescales, ranging from hours to months. In specific cases, such variability can profoundly impact the response of cell populations to therapy, in both antibiotic treatments in bacteria and chemotherapy in cancer. It is thus critical to understand the way phenotypes fluctuate in cell populations and the molecular sources of phenotypic diversity. Technical and analytical breakthroughs in the study of single cells have leveraged cellular heterogeneity to gain phenomenological and mechanistic insights of the phenotypic transitions that occur within isogenic cell populations over time. Such an understanding moves forward our ability to design therapeutic strategies with the explicit goal of preventing and controlling the selective expansion and stabilization of drug-tolerant phenotypic states.
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Affiliation(s)
- José Reyes
- Department of Systems Biology, Harvard Medical School, Boston MA, USA; Systems Biology PhD Program, Harvard University, Cambridge MA, USA
| | - Galit Lahav
- Department of Systems Biology, Harvard Medical School, Boston MA, USA.
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18
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Cooper S, Barr AR, Glen R, Bakal C. NucliTrack: an integrated nuclei tracking application. Bioinformatics 2017; 33:3320-3322. [PMID: 28637183 PMCID: PMC5860035 DOI: 10.1093/bioinformatics/btx404] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 05/22/2017] [Accepted: 06/17/2017] [Indexed: 11/13/2022] Open
Abstract
SUMMARY Live imaging studies give unparalleled insight into dynamic single cell behaviours and fate decisions. However, the challenge of reliably tracking single cells over long periods of time limits both the throughput and ease with which such studies can be performed. Here, we present NucliTrack, a cross platform solution for automatically segmenting, tracking and extracting features from fluorescently labelled nuclei. NucliTrack performs similarly to other state-of-the-art cell tracking algorithms, but NucliTrack's interactive, graphical interface makes it significantly more user friendly. AVAILABILITY AND IMPLEMENTATION NucliTrack is available as a free, cross platform application and open source Python package. Installation details and documentation are at: http://nuclitrack.readthedocs.io/en/latest/ A video guide can be viewed online: https://www.youtube.com/watch?v=J6e0D9F-qSU Source code is available through Github: https://github.com/samocooper/nuclitrack. A Matlab toolbox is also available at: https://uk.mathworks.com/matlabcentral/fileexchange/61479-samocooper-nuclitrack-matlab. CONTACT sam@socooper.com. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sam Cooper
- Division of Cancer Biology, The Institute of Cancer Research, London, UK
- Department of Computational Systems Medicine, Imperial College, South Kensington Campus, London, UK
| | - Alexis R Barr
- Division of Cancer Biology, The Institute of Cancer Research, London, UK
| | - Robert Glen
- Department of Computational Systems Medicine, Imperial College, South Kensington Campus, London, UK
| | - Chris Bakal
- Division of Cancer Biology, The Institute of Cancer Research, London, UK
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