51
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Alfonso-Garcia A, Bec J, Weyers B, Marsden M, Zhou X, Li C, Marcu L. Mesoscopic fluorescence lifetime imaging: Fundamental principles, clinical applications and future directions. JOURNAL OF BIOPHOTONICS 2021; 14:e202000472. [PMID: 33710785 PMCID: PMC8579869 DOI: 10.1002/jbio.202000472] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 03/03/2021] [Accepted: 03/05/2021] [Indexed: 05/16/2023]
Abstract
Fluorescence lifetime imaging (FLIm) is an optical spectroscopic imaging technique capable of real-time assessments of tissue properties in clinical settings. Label-free FLIm is sensitive to changes in tissue structure and biochemistry resulting from pathological conditions, thus providing optical contrast to identify and monitor the progression of disease. Technical and methodological advances over the last two decades have enabled the development of FLIm instrumentation for real-time, in situ, mesoscopic imaging compatible with standard clinical workflows. Herein, we review the fundamental working principles of mesoscopic FLIm, discuss the technical characteristics of current clinical FLIm instrumentation, highlight the most commonly used analytical methods to interpret fluorescence lifetime data and discuss the recent applications of FLIm in surgical oncology and cardiovascular diagnostics. Finally, we conclude with an outlook on the future directions of clinical FLIm.
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Affiliation(s)
- Alba Alfonso-Garcia
- Department of Biomedical Engineering, University of California, Davis, Davis, California
| | - Julien Bec
- Department of Biomedical Engineering, University of California, Davis, Davis, California
| | - Brent Weyers
- Department of Biomedical Engineering, University of California, Davis, Davis, California
| | - Mark Marsden
- Department of Biomedical Engineering, University of California, Davis, Davis, California
| | - Xiangnan Zhou
- Department of Biomedical Engineering, University of California, Davis, Davis, California
| | - Cai Li
- Department of Biomedical Engineering, University of California, Davis, Davis, California
| | - Laura Marcu
- Department of Biomedical Engineering, University of California, Davis, Davis, California
- Department Neurological Surgery, University of California, Davis, California
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52
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Wang K, Liu L, Mao D, Xu S, Tan C, Cao Q, Mao Z, Liu B. A Polarity‐Sensitive Ratiometric Fluorescence Probe for Monitoring Changes in Lipid Droplets and Nucleus during Ferroptosis. Angew Chem Int Ed Engl 2021. [DOI: 10.1002/ange.202104163] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Kang‐Nan Wang
- MOE Key Laboratory of Bioinorganic and Synthetic Chemistry School of Chemistry Sun Yat-Sen University Guangzhou 510275 P. R. China
- Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 Singapore 117585 Singapore
| | - Liu‐Yi Liu
- MOE Key Laboratory of Bioinorganic and Synthetic Chemistry School of Chemistry Sun Yat-Sen University Guangzhou 510275 P. R. China
| | - Duo Mao
- Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 Singapore 117585 Singapore
| | - Shidang Xu
- Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 Singapore 117585 Singapore
| | - Cai‐Ping Tan
- MOE Key Laboratory of Bioinorganic and Synthetic Chemistry School of Chemistry Sun Yat-Sen University Guangzhou 510275 P. R. China
| | - Qian Cao
- MOE Key Laboratory of Bioinorganic and Synthetic Chemistry School of Chemistry Sun Yat-Sen University Guangzhou 510275 P. R. China
| | - Zong‐Wan Mao
- MOE Key Laboratory of Bioinorganic and Synthetic Chemistry School of Chemistry Sun Yat-Sen University Guangzhou 510275 P. R. China
| | - Bin Liu
- Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 Singapore 117585 Singapore
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53
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Wang KN, Liu LY, Mao D, Xu S, Tan CP, Cao Q, Mao ZW, Liu B. A Polarity-Sensitive Ratiometric Fluorescence Probe for Monitoring Changes in Lipid Droplets and Nucleus during Ferroptosis. Angew Chem Int Ed Engl 2021; 60:15095-15100. [PMID: 33835669 DOI: 10.1002/anie.202104163] [Citation(s) in RCA: 137] [Impact Index Per Article: 45.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Indexed: 01/08/2023]
Abstract
Ferroptosis regulates cell death through reactive oxygen species (ROS)-associated lipid peroxide accumulation, which is expected to affect the structure and polarity of lipid droplets (LDs), but with no clear evidence. Herein, we report the first example of an LD/nucleus dual-targeted ratiometric fluorescent probe, CQPP, for monitoring polarity changes in the cellular microenvironment. Due to the donor-acceptor structure of CQPP, it offers ratiometric fluorescence emission and fluorescence lifetime signals that reflect polarity variations. Using nucleus imaging as a reference, CQPP was applied to report the increase in LD polarity and the homogenization of polarity between LDs and cytoplasm in the ferroptosis model. This LD/nucleus dual-targeted fluorescent probe shows the great potential of using fluorescence imaging to study ferroptosis and ferroptosis-related diseases.
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Affiliation(s)
- Kang-Nan Wang
- MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, Sun Yat-Sen University, Guangzhou, 510275, P. R. China.,Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore
| | - Liu-Yi Liu
- MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, Sun Yat-Sen University, Guangzhou, 510275, P. R. China
| | - Duo Mao
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore
| | - Shidang Xu
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore
| | - Cai-Ping Tan
- MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, Sun Yat-Sen University, Guangzhou, 510275, P. R. China
| | - Qian Cao
- MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, Sun Yat-Sen University, Guangzhou, 510275, P. R. China
| | - Zong-Wan Mao
- MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, Sun Yat-Sen University, Guangzhou, 510275, P. R. China
| | - Bin Liu
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore
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54
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Mukherjee L, Sagar MAK, Ouellette JN, Watters JJ, Eliceiri KW. Joint regression-classification deep learning framework for analyzing fluorescence lifetime images using NADH and FAD. BIOMEDICAL OPTICS EXPRESS 2021; 12:2703-2719. [PMID: 34123498 PMCID: PMC8176805 DOI: 10.1364/boe.417108] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 02/21/2021] [Accepted: 03/30/2021] [Indexed: 06/12/2023]
Abstract
In this paper, we develop a deep neural network based joint classification-regression approach to identify microglia, a resident central nervous system macrophage, in the brain using fluorescence lifetime imaging microscopy (FLIM) data. Microglia are responsible for several key aspects of brain development and neurodegenerative diseases. Accurate detection of microglia is key to understanding their role and function in the CNS, and has been studied extensively in recent years. In this paper, we propose a joint classification-regression scheme that can incorporate fluorescence lifetime data from two different autofluorescent metabolic co-enzymes, FAD and NADH, in the same model. This approach not only represents the lifetime data more accurately but also provides the classification engine a more diverse data source. Furthermore, the two components of model can be trained jointly which combines the strengths of the regression and classification methods. We demonstrate the efficacy of our method using datasets generated using mouse brain tissue which show that our joint learning model outperforms results on the coenzymes taken independently, providing an efficient way to classify microglia from other cells.
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Affiliation(s)
- Lopamudra Mukherjee
- Department of Computer Science, University of Wisconsin Whitewater, Whitewater WI 53190, USA
- Co-corresponding authors
| | - Md Abdul Kader Sagar
- Laboratory for Optical and Computational Instrumentation, Department of Biomedical Engineering, University of Wisconsin Madison, Madison, WI 53705, USA
| | - Jonathan N Ouellette
- Laboratory for Optical and Computational Instrumentation, Department of Biomedical Engineering, University of Wisconsin Madison, Madison, WI 53705, USA
| | - Jyoti J Watters
- Department of Comparative Biosciences, University of Wisconsin Madison, Madison, WI 53705, USA
| | - Kevin W Eliceiri
- Laboratory for Optical and Computational Instrumentation, Department of Biomedical Engineering, University of Wisconsin Madison, Madison, WI 53705, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Morgridge Institute for Research, Madison, WI 53706, USA
- Co-corresponding authors
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55
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Dmitriev RI, Intes X, Barroso MM. Luminescence lifetime imaging of three-dimensional biological objects. J Cell Sci 2021; 134:1-17. [PMID: 33961054 PMCID: PMC8126452 DOI: 10.1242/jcs.254763] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
A major focus of current biological studies is to fill the knowledge gaps between cell, tissue and organism scales. To this end, a wide array of contemporary optical analytical tools enable multiparameter quantitative imaging of live and fixed cells, three-dimensional (3D) systems, tissues, organs and organisms in the context of their complex spatiotemporal biological and molecular features. In particular, the modalities of luminescence lifetime imaging, comprising fluorescence lifetime imaging (FLI) and phosphorescence lifetime imaging microscopy (PLIM), in synergy with Förster resonance energy transfer (FRET) assays, provide a wealth of information. On the application side, the luminescence lifetime of endogenous molecules inside cells and tissues, overexpressed fluorescent protein fusion biosensor constructs or probes delivered externally provide molecular insights at multiple scales into protein-protein interaction networks, cellular metabolism, dynamics of molecular oxygen and hypoxia, physiologically important ions, and other physical and physiological parameters. Luminescence lifetime imaging offers a unique window into the physiological and structural environment of cells and tissues, enabling a new level of functional and molecular analysis in addition to providing 3D spatially resolved and longitudinal measurements that can range from microscopic to macroscopic scale. We provide an overview of luminescence lifetime imaging and summarize key biological applications from cells and tissues to organisms.
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Affiliation(s)
- Ruslan I. Dmitriev
- Tissue Engineering and Biomaterials Group, Department of
Human Structure and Repair, Faculty of Medicine and Health Sciences,
Ghent University, Ghent 9000,
Belgium
| | - Xavier Intes
- Department of Biomedical Engineering, Center for
Modeling, Simulation and Imaging for Medicine (CeMSIM),
Rensselaer Polytechnic Institute, Troy, NY
12180-3590, USA
| | - Margarida M. Barroso
- Department of Molecular and Cellular
Physiology, Albany Medical College,
Albany, NY 12208, USA
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56
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Bitton A, Sambrano J, Valentino S, Houston JP. A Review of New High-Throughput Methods Designed for Fluorescence Lifetime Sensing From Cells and Tissues. FRONTIERS IN PHYSICS 2021; 9:648553. [PMID: 34007839 PMCID: PMC8127321 DOI: 10.3389/fphy.2021.648553] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
Though much of the interest in fluorescence in the past has been on measuring spectral qualities such as wavelength and intensity, there are two other highly useful intrinsic properties of fluorescence: lifetime (or decay) and anisotropy (or polarization). Each has its own set of unique advantages, limitations, and challenges in detection when it comes to use in biological studies. This review will focus on the property of fluorescence lifetime, providing a brief background on instrumentation and theory, and examine the recent advancements and applications of measuring lifetime in the fields of high-throughput fluorescence lifetime imaging microscopy (HT-FLIM) and time-resolved flow cytometry (TRFC). In addition, the crossover of these two methods and their outlooks will be discussed.
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Affiliation(s)
- Aric Bitton
- Department of Chemical and Materials Engineering, New Mexico State University, Las Cruces, NM, United States
| | - Jesus Sambrano
- Department of Chemical and Materials Engineering, New Mexico State University, Las Cruces, NM, United States
| | - Samantha Valentino
- Department of Chemical and Materials Engineering, New Mexico State University, Las Cruces, NM, United States
| | - Jessica P. Houston
- Department of Chemical and Materials Engineering, New Mexico State University, Las Cruces, NM, United States
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57
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Fischer LS, Rangarajan S, Sadhanasatish T, Grashoff C. Molecular Force Measurement with Tension Sensors. Annu Rev Biophys 2021; 50:595-616. [PMID: 33710908 DOI: 10.1146/annurev-biophys-101920-064756] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The ability of cells to generate mechanical forces, but also to sense, adapt to, and respond to mechanical signals, is crucial for many developmental, postnatal homeostatic, and pathophysiological processes. However, the molecular mechanisms underlying cellular mechanotransduction have remained elusive for many decades, as techniques to visualize and quantify molecular forces across individual proteins in cells were missing. The development of genetically encoded molecular tension sensors now allows the quantification of piconewton-scale forces that act upon distinct molecules in living cells and even whole organisms. In this review, we discuss the physical principles, advantages, and limitations of this increasingly popular method. By highlighting current examples from the literature, we demonstrate how molecular tension sensors can be utilized to obtain access to previously unappreciated biophysical parameters that define the propagation of mechanical forces on molecular scales. We discuss how the methodology can be further developed and provide a perspective on how the technique could be applied to uncover entirely novel aspects of mechanobiology in the future.
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Affiliation(s)
- Lisa S Fischer
- Department of Quantitative Cell Biology, Institute of Molecular Cell Biology, University of Münster, Münster D-48149, Germany;
| | - Srishti Rangarajan
- Department of Quantitative Cell Biology, Institute of Molecular Cell Biology, University of Münster, Münster D-48149, Germany;
| | - Tanmay Sadhanasatish
- Department of Quantitative Cell Biology, Institute of Molecular Cell Biology, University of Münster, Münster D-48149, Germany;
| | - Carsten Grashoff
- Department of Quantitative Cell Biology, Institute of Molecular Cell Biology, University of Münster, Münster D-48149, Germany;
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58
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Michalet X. An overview of continuous and discrete phasor analysis of binned or time-gated periodic decays. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2021; 11648:116480E. [PMID: 33854272 PMCID: PMC8041354 DOI: 10.1117/12.2577747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Time-resolved analysis of periodically excited luminescence decays by the phasor method in the presence of time-gating or binning is revisited. Analytical expressions for discrete configurations of square gates are derived and the locus of the phasors of such modified periodic single-exponential decays is compared to the canonical universal semicircle. The effects of IRF offset, decay truncation and gate shape are also discussed. Finally, modified expressions for the phase and modulus lifetimes are provided for some simple cases. A discussion of a modified phasor calibration approach is presented.
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Affiliation(s)
- X Michalet
- Department of Chemistry & Biochemistry, 607 Charles E. Young Drive E., Los Angeles, CA 90095, USA
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59
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Michalet X. Continuous and discrete phasor analysis of binned or time-gated periodic decays. AIP ADVANCES 2021; 11:035331. [PMID: 33786208 PMCID: PMC7990508 DOI: 10.1063/5.0027834] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 01/22/2021] [Indexed: 05/29/2023]
Abstract
The time-resolved analysis of periodically excited luminescence decays by the phasor method in the presence of time-gating or binning is revisited. Analytical expressions for discrete configurations of square gates are derived, and the locus of the phasors of such modified periodic single-exponential decays is compared to the canonical universal semicircle. The effects of instrument response function offset, decay truncation, and gate shape are also discussed. Finally, modified expressions for the phase and modulus lifetimes are provided for some simple cases. A discussion of a modified phasor calibration approach is presented, and an illustration of the new concepts with examples from the literature concludes this work.
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Affiliation(s)
- Xavier Michalet
- Department of Chemistry and Biochemistry, 607 Charles E. Young Drive E., Los Angeles, California 90095, USA
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60
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He H, Yan S, Lyu D, Xu M, Ye R, Zheng P, Lu X, Wang L, Ren B. Deep Learning for Biospectroscopy and Biospectral Imaging: State-of-the-Art and Perspectives. Anal Chem 2021; 93:3653-3665. [PMID: 33599125 DOI: 10.1021/acs.analchem.0c04671] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
With the advances in instrumentation and sampling techniques, there is an explosive growth of data from molecular and cellular samples. The call to extract more information from the large data sets has greatly challenged the conventional chemometrics method. Deep learning, which utilizes very large data sets for finding hidden features therein and for making accurate predictions for a wide range of applications, has been applied in an unbelievable pace in biospectroscopy and biospectral imaging in the recent 3 years. In this Feature, we first introduce the background and basic knowledge of deep learning. We then focus on the emerging applications of deep learning in the data preprocessing, feature detection, and modeling of the biological samples for spectral analysis and spectroscopic imaging. Finally, we highlight the challenges and limitations in deep learning and the outlook for future directions.
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Affiliation(s)
- Hao He
- School of Aerospace Engineering, Xiamen University, Xiamen, 361000, China
| | - Sen Yan
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Danya Lyu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Mengxi Xu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Ruiqian Ye
- School of Aerospace Engineering, Xiamen University, Xiamen, 361000, China
| | - Peng Zheng
- School of Aerospace Engineering, Xiamen University, Xiamen, 361000, China
| | - Xinyu Lu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Lei Wang
- School of Aerospace Engineering, Xiamen University, Xiamen, 361000, China
| | - Bin Ren
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
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Guo S, Silge A, Bae H, Tolstik T, Meyer T, Matziolis G, Schmitt M, Popp J, Bocklitz T. FLIM data analysis based on Laguerre polynomial decomposition and machine-learning. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-200186SSR. [PMID: 33415850 PMCID: PMC7790506 DOI: 10.1117/1.jbo.26.2.022909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 12/11/2020] [Indexed: 05/03/2023]
Abstract
SIGNIFICANCE The potential of fluorescence lifetime imaging microscopy (FLIM) is recently being recognized, especially in biological studies. However, FLIM does not directly measure the lifetimes, rather it records the fluorescence decay traces. The lifetimes and/or abundances have to be estimated from these traces during the phase of data processing. To precisely estimate these parameters is challenging and requires a well-designed computer program. Conventionally employed methods, which are based on curve fitting, are computationally expensive and limited in performance especially for highly noisy FLIM data. The graphical analysis, while free of fit, requires calibration samples for a quantitative analysis. AIM We propose to extract the lifetimes and abundances directly from the decay traces through machine learning (ML). APPROACH The ML-based approach was verified with simulated testing data in which the lifetimes and abundances were known exactly. Thereafter, we compared its performance with the commercial software SPCImage based on datasets measured from biological samples on a time-correlated single photon counting system. We reconstructed the decay traces using the lifetime and abundance values estimated by ML and SPCImage methods and utilized the root-mean-squared-error (RMSE) as marker. RESULTS The RMSE, which represents the difference between the reconstructed and measured decay traces, was observed to be lower for ML than for SPCImage. In addition, we could demonstrate with a three-component analysis the high potential and flexibility of the ML method to deal with more than two lifetime components. CONCLUSIONS The ML-based approach shows great performance in FLIM data analysis.
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Affiliation(s)
- Shuxia Guo
- University of Jena, Institute of Physical Chemistry, Abbe Center of Photonics, Jena, Germany
- Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Jena, Germany
| | - Anja Silge
- University of Jena, Institute of Physical Chemistry, Abbe Center of Photonics, Jena, Germany
- Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Jena, Germany
| | - Hyeonsoo Bae
- University of Jena, Institute of Physical Chemistry, Abbe Center of Photonics, Jena, Germany
- Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Jena, Germany
| | - Tatiana Tolstik
- University of Jena, Institute of Physical Chemistry, Abbe Center of Photonics, Jena, Germany
- Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Jena, Germany
| | - Tobias Meyer
- University of Jena, Institute of Physical Chemistry, Abbe Center of Photonics, Jena, Germany
- Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Jena, Germany
| | | | - Michael Schmitt
- University of Jena, Institute of Physical Chemistry, Abbe Center of Photonics, Jena, Germany
- Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Jena, Germany
| | - Jürgen Popp
- University of Jena, Institute of Physical Chemistry, Abbe Center of Photonics, Jena, Germany
- Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Jena, Germany
| | - Thomas Bocklitz
- University of Jena, Institute of Physical Chemistry, Abbe Center of Photonics, Jena, Germany
- Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Jena, Germany
- Address all correspondence to Thomas Bocklitz,
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Quantification of Trastuzumab-HER2 Engagement In Vitro and In Vivo. Molecules 2020; 25:molecules25245976. [PMID: 33348564 PMCID: PMC7767145 DOI: 10.3390/molecules25245976] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 11/22/2020] [Accepted: 12/11/2020] [Indexed: 12/12/2022] Open
Abstract
Human EGF Receptor 2 (HER2) is an important oncogene driving aggressive metastatic growth in up to 20% of breast cancer tumors. At the same time, it presents a target for passive immunotherapy such as trastuzumab (TZM). Although TZM has been widely used clinically since 1998, not all eligible patients benefit from this therapy due to primary and acquired drug resistance as well as potentially lack of drug exposure. Hence, it is critical to directly quantify TZM–HER2 binding dynamics, also known as cellular target engagement, in undisturbed tumor environments in live, intact tumor xenograft models. Herein, we report the direct measurement of TZM–HER2 binding in HER2-positive human breast cancer cells and tumor xenografts using fluorescence lifetime Forster Resonance Energy Transfer (FLI-FRET) via near-infrared (NIR) microscopy (FLIM-FRET) as well as macroscopy (MFLI-FRET) approaches. By sensing the reduction of fluorescence lifetime of donor-labeled TZM in the presence of acceptor-labeled TZM, we successfully quantified the fraction of HER2-bound and internalized TZM immunoconjugate both in cell culture and tumor xenografts in live animals. Ex vivo immunohistological analysis of tumors confirmed the binding and internalization of TZM–HER2 complex in breast cancer cells. Thus, FLI-FRET imaging presents a powerful analytical tool to monitor and quantify cellular target engagement and subsequent intracellular drug delivery in live HER2-positive tumor xenografts.
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63
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Zickus V, Wu ML, Morimoto K, Kapitany V, Fatima A, Turpin A, Insall R, Whitelaw J, Machesky L, Bruschini C, Faccio D, Charbon E. Fluorescence lifetime imaging with a megapixel SPAD camera and neural network lifetime estimation. Sci Rep 2020; 10:20986. [PMID: 33268900 PMCID: PMC7710711 DOI: 10.1038/s41598-020-77737-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Accepted: 11/06/2020] [Indexed: 01/07/2023] Open
Abstract
Fluorescence lifetime imaging microscopy (FLIM) is a key technology that provides direct insight into cell metabolism, cell dynamics and protein activity. However, determining the lifetimes of different fluorescent proteins requires the detection of a relatively large number of photons, hence slowing down total acquisition times. Moreover, there are many cases, for example in studies of cell collectives, where wide-field imaging is desired. We report scan-less wide-field FLIM based on a 0.5 MP resolution, time-gated Single Photon Avalanche Diode (SPAD) camera, with acquisition rates up to 1 Hz. Fluorescence lifetime estimation is performed via a pre-trained artificial neural network with 1000-fold improvement in processing times compared to standard least squares fitting techniques. We utilised our system to image HT1080-human fibrosarcoma cell line as well as Convallaria. The results show promise for real-time FLIM and a viable route towards multi-megapixel fluorescence lifetime images, with a proof-of-principle mosaic image shown with 3.6 MP.
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Affiliation(s)
- Vytautas Zickus
- School of Physics and Astronomy, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Ming-Lo Wu
- Advanced Quantum Architecture Laboratory, Ecole Polytechnique Fédérale de Lausanne, 2002, Neuchâtel, Switzerland
| | - Kazuhiro Morimoto
- Advanced Quantum Architecture Laboratory, Ecole Polytechnique Fédérale de Lausanne, 2002, Neuchâtel, Switzerland
| | - Valentin Kapitany
- School of Physics and Astronomy, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Areeba Fatima
- School of Physics and Astronomy, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Alex Turpin
- School of Computing Science, University of Glasgow, Glasgow, G12 8LT, UK
| | - Robert Insall
- University of Glasgow Institute of Cancer Sciences, Glasgow, UK.,Cancer Research UK, Beatson Institute, Glasgow, UK
| | - Jamie Whitelaw
- University of Glasgow Institute of Cancer Sciences, Glasgow, UK.,Cancer Research UK, Beatson Institute, Glasgow, UK
| | - Laura Machesky
- University of Glasgow Institute of Cancer Sciences, Glasgow, UK.,Cancer Research UK, Beatson Institute, Glasgow, UK
| | - Claudio Bruschini
- Advanced Quantum Architecture Laboratory, Ecole Polytechnique Fédérale de Lausanne, 2002, Neuchâtel, Switzerland
| | - Daniele Faccio
- School of Physics and Astronomy, University of Glasgow, Glasgow, G12 8QQ, UK.
| | - Edoardo Charbon
- Advanced Quantum Architecture Laboratory, Ecole Polytechnique Fédérale de Lausanne, 2002, Neuchâtel, Switzerland.
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64
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Thomsen J, Sletfjerding MB, Jensen SB, Stella S, Paul B, Malle MG, Montoya G, Petersen TC, Hatzakis NS. DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning. eLife 2020; 9:e60404. [PMID: 33138911 PMCID: PMC7609065 DOI: 10.7554/elife.60404] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 10/02/2020] [Indexed: 12/20/2022] Open
Abstract
Single-molecule Förster Resonance energy transfer (smFRET) is an adaptable method for studying the structure and dynamics of biomolecules. The development of high throughput methodologies and the growth of commercial instrumentation have outpaced the development of rapid, standardized, and automated methodologies to objectively analyze the wealth of produced data. Here we present DeepFRET, an automated, open-source standalone solution based on deep learning, where the only crucial human intervention in transiting from raw microscope images to histograms of biomolecule behavior, is a user-adjustable quality threshold. Integrating standard features of smFRET analysis, DeepFRET consequently outputs the common kinetic information metrics. Its classification accuracy on ground truth data reached >95% outperforming human operators and commonly used threshold, only requiring ~1% of the time. Its precise and rapid operation on real data demonstrates DeepFRET's capacity to objectively quantify biomolecular dynamics and the potential to contribute to benchmarking smFRET for dynamic structural biology.
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Affiliation(s)
- Johannes Thomsen
- Department of Chemistry and Nanoscience Centre, University of CopenhagenCopenhagenDenmark
| | | | - Simon Bo Jensen
- Department of Chemistry and Nanoscience Centre, University of CopenhagenCopenhagenDenmark
| | - Stefano Stella
- Structural Molecular Biology Group, Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of CopenhagenCopenhagenDenmark
| | - Bijoya Paul
- Structural Molecular Biology Group, Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of CopenhagenCopenhagenDenmark
| | - Mette Galsgaard Malle
- Department of Chemistry and Nanoscience Centre, University of CopenhagenCopenhagenDenmark
| | - Guillermo Montoya
- Structural Molecular Biology Group, Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of CopenhagenCopenhagenDenmark
| | | | - Nikos S Hatzakis
- Department of Chemistry and Nanoscience Centre, University of CopenhagenCopenhagenDenmark
- Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of CopenhagenCopenhagenDenmark
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65
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Fung AA, Shi L. Mammalian cell and tissue imaging using Raman and coherent Raman microscopy. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2020; 12:e1501. [PMID: 32686297 PMCID: PMC7554227 DOI: 10.1002/wsbm.1501] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 05/29/2020] [Accepted: 06/08/2020] [Indexed: 12/16/2022]
Abstract
Direct imaging of metabolism in cells or multicellular organisms is important for understanding many biological processes. Raman scattering (RS) microscopy, particularly, coherent Raman scattering (CRS) such as coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS), has emerged as a powerful platform for cellular imaging due to its high chemical selectivity, sensitivity, and imaging speed. RS microscopy has been extensively used for the identification of subcellular structures, metabolic observation, and phenotypic characterization. Conjugating RS modalities with other techniques such as fluorescence or infrared (IR) spectroscopy, flow cytometry, and RNA-sequencing can further extend the applications of RS imaging in microbiology, system biology, neurology, tumor biology and more. Here we overview RS modalities and techniques for mammalian cell and tissue imaging, with a focus on the advances and applications of CARS and SRS microscopy, for a better understanding of the metabolism and dynamics of lipids, protein, glucose, and nucleic acids in mammalian cells and tissues. This article is categorized under: Laboratory Methods and Technologies > Imaging Biological Mechanisms > Metabolism Analytical and Computational Methods > Analytical Methods.
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Affiliation(s)
- Anthony A Fung
- Department of Bioengineering, University of California San Diego, La Jolla, California, USA
| | - Lingyan Shi
- Department of Bioengineering, University of California San Diego, La Jolla, California, USA
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66
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Ochoa M, Rudkouskaya A, Yao R, Yan P, Barroso M, Intes X. High compression deep learning based single-pixel hyperspectral macroscopic fluorescence lifetime imaging in vivo. BIOMEDICAL OPTICS EXPRESS 2020; 11:5401-5424. [PMID: 33149959 PMCID: PMC7587256 DOI: 10.1364/boe.396771] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 07/02/2020] [Accepted: 07/15/2020] [Indexed: 05/05/2023]
Abstract
Single pixel imaging frameworks facilitate the acquisition of high-dimensional optical data in biological applications with photon starved conditions. However, they are still limited to slow acquisition times and low pixel resolution. Herein, we propose a convolutional neural network for fluorescence lifetime imaging with compressed sensing at high compression (NetFLICS-CR), which enables in vivo applications at enhanced resolution, acquisition and processing speeds, without the need for experimental training datasets. NetFLICS-CR produces intensity and lifetime reconstructions at 128 × 128 pixel resolution over 16 spectral channels while using only up to 1% of the required measurements, therefore reducing acquisition times from ∼2.5 hours at 50% compression to ∼3 minutes at 99% compression. Its potential is demonstrated in silico, in vitro and for mice in vivo through the monitoring of receptor-ligand interactions in liver and bladder and further imaging of intracellular delivery of the clinical drug Trastuzumab to HER2-positive breast tumor xenografts. The data acquisition time and resolution improvement through NetFLICS-CR, facilitate the translation of single pixel macroscopic flurorescence lifetime imaging (SP-MFLI) for in vivo monitoring of lifetime properties and drug uptake.
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Affiliation(s)
- M. Ochoa
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - A. Rudkouskaya
- Department of Molecular and Cellular Physiology, Albany Medical College, Albany, NY 12208, USA
| | - R. Yao
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - P. Yan
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - M. Barroso
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - X. Intes
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
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67
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Aguénounon E, Smith JT, Al-Taher M, Diana M, Intes X, Gioux S. Real-time, wide-field and high-quality single snapshot imaging of optical properties with profile correction using deep learning. BIOMEDICAL OPTICS EXPRESS 2020; 11:5701-5716. [PMID: 33149980 PMCID: PMC7587245 DOI: 10.1364/boe.397681] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 08/28/2020] [Accepted: 08/28/2020] [Indexed: 05/06/2023]
Abstract
The development of real-time, wide-field and quantitative diffuse optical imaging methods to visualize functional and structural biomarkers of living tissues is a pressing need for numerous clinical applications including image-guided surgery. In this context, Spatial Frequency Domain Imaging (SFDI) is an attractive method allowing for the fast estimation of optical properties using the Single Snapshot of Optical Properties (SSOP) approach. Herein, we present a novel implementation of SSOP based on a combination of deep learning network at the filtering stage and Graphics Processing Units (GPU) capable of simultaneous high visual quality image reconstruction, surface profile correction and accurate optical property (OP) extraction in real-time across large fields of view. In the most optimal implementation, the presented methodology demonstrates megapixel profile-corrected OP imaging with results comparable to that of profile-corrected SFDI, with a processing time of 18 ms and errors relative to SFDI method less than 10% in both profilometry and profile-corrected OPs. This novel processing framework lays the foundation for real-time multispectral quantitative diffuse optical imaging for surgical guidance and healthcare applications. All code and data used for this work is publicly available at www.healthphotonics.org under the resources tab.
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Affiliation(s)
- Enagnon Aguénounon
- University of Strasbourg, ICube Laboratory, 300 Boulevard Sébastien Brant, 67412 Illkirch, France
| | - Jason T. Smith
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Mahdi Al-Taher
- Institute of Image-Guided Surgery, IHU Strasbourg, Strasbourg, France
- Maastricht University Medical Center, Maastricht, The Netherlands
| | - Michele Diana
- University of Strasbourg, ICube Laboratory, 300 Boulevard Sébastien Brant, 67412 Illkirch, France
- Institute of Image-Guided Surgery, IHU Strasbourg, Strasbourg, France
- Research Institute against Digestive Cancer, IRCAD, Strasbourg, France
| | - Xavier Intes
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Sylvain Gioux
- University of Strasbourg, ICube Laboratory, 300 Boulevard Sébastien Brant, 67412 Illkirch, France
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68
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Poon CS, Long F, Sunar U. Deep learning model for ultrafast quantification of blood flow in diffuse correlation spectroscopy. BIOMEDICAL OPTICS EXPRESS 2020; 11:5557-5564. [PMID: 33149970 PMCID: PMC7587273 DOI: 10.1364/boe.402508] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 08/24/2020] [Accepted: 08/26/2020] [Indexed: 06/01/2023]
Abstract
Diffuse correlation spectroscopy (DCS) is increasingly used in the optical imaging field to assess blood flow in humans due to its non-invasive, real-time characteristics and its ability to provide label-free, bedside monitoring of blood flow changes. Previous DCS studies have utilized a traditional curve fitting of the analytical or Monte Carlo models to extract the blood flow changes, which are computationally demanding and less accurate when the signal to noise ratio decreases. Here, we present a deep learning model that eliminates this bottleneck by solving the inverse problem more than 2300% faster, with equivalent or improved accuracy compared to the nonlinear fitting with an analytical method. The proposed deep learning inverse model will enable real-time and accurate tissue blood flow quantification with the DCS technique.
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Affiliation(s)
- Chien-Sing Poon
- Department of Biomedical Engineering, Wright State
University, 207 Russ Engineering Center, 3640 Colonel Glenn Hwy.,
Dayton, OH 45435, USA
| | | | - Ulas Sunar
- Department of Biomedical Engineering, Wright State
University, 207 Russ Engineering Center, 3640 Colonel Glenn Hwy.,
Dayton, OH 45435, USA
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69
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Sagar MAK, Cheng KP, Ouellette JN, Williams JC, Watters JJ, Eliceiri KW. Machine Learning Methods for Fluorescence Lifetime Imaging (FLIM) Based Label-Free Detection of Microglia. Front Neurosci 2020; 14:931. [PMID: 33013309 PMCID: PMC7497798 DOI: 10.3389/fnins.2020.00931] [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: 06/15/2019] [Accepted: 08/11/2020] [Indexed: 12/22/2022] Open
Abstract
Automated computational analysis techniques utilizing machine learning have been demonstrated to be able to extract more data from different imaging modalities compared to traditional analysis techniques. One new approach is to use machine learning techniques to existing multiphoton imaging modalities to better interpret intrinsically fluorescent cellular signals to characterize different cell types. Fluorescence Lifetime Imaging Microscopy (FLIM) is a high-resolution quantitative imaging tool that can detect metabolic cellular signatures based on the lifetime variations of intrinsically fluorescent metabolic co-factors such as nicotinamide adenine dinucleotide [NAD(P)H]. NAD(P)H lifetime-based discrimination techniques have previously been used to develop metabolic cell signatures for diverse cell types including immune cells such as macrophages. However, FLIM could be even more effective in characterizing cell types if machine learning was used to classify cells by utilizing FLIM parameters for classification. Here, we demonstrate the potential for FLIM-based, label-free NAD(P)H imaging to distinguish different cell types using Artificial Neural Network (ANN)-based machine learning. For our biological use case, we used the challenge of differentiating microglia from other glia cell types in the brain. Microglia are the resident macrophages of the brain and spinal cord and play a critical role in maintaining the neural environment and responding to injury. Microglia are challenging to identify as most fluorescent labeling approaches cross-react with other immune cell types, are often insensitive to activation state, and require the use of multiple specialized antibody labels. Furthermore, the use of these extrinsic antibody labels prevents application in in vivo animal models and possible future clinical adaptations such as neurodegenerative pathologies. With the ANN-based NAD(P)H FLIM analysis approach, we found that microglia in cell culture mixed with other glial cells can be identified with more than 0.9 True Positive Rate (TPR). We also extended our approach to identify microglia in fixed brain tissue with a TPR of 0.79. In both cases the False Discovery Rate was around 30%. This method can be further extended to potentially study and better understand microglia’s role in neurodegenerative disease with improved detection accuracy.
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Affiliation(s)
- Md Abdul Kader Sagar
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States.,Laboratory for Optical and Computational Instrumentation, University of Wisconsin-Madison, Madison, WI, United States
| | - Kevin P Cheng
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Jonathan N Ouellette
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States.,Laboratory for Optical and Computational Instrumentation, University of Wisconsin-Madison, Madison, WI, United States
| | - Justin C Williams
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Jyoti J Watters
- Department of Comparative Biosciences, University of Wisconsin-Madison, Madison, WI, United States
| | - Kevin W Eliceiri
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States.,Laboratory for Optical and Computational Instrumentation, University of Wisconsin-Madison, Madison, WI, United States.,Morgridge Institute for Research, Madison, WI, United States
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70
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Rudkouskaya A, Sinsuebphon N, Ochoa M, Chen SJ, Mazurkiewicz JE, Intes X, Barroso M. Multiplexed non-invasive tumor imaging of glucose metabolism and receptor-ligand engagement using dark quencher FRET acceptor. Theranostics 2020; 10:10309-10325. [PMID: 32929350 PMCID: PMC7481426 DOI: 10.7150/thno.45825] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 07/25/2020] [Indexed: 12/31/2022] Open
Abstract
Rationale: Following an ever-increased focus on personalized medicine, there is a continuing need to develop preclinical molecular imaging modalities to guide the development and optimization of targeted therapies. Near-Infrared (NIR) Macroscopic Fluorescence Lifetime Förster Resonance Energy Transfer (MFLI-FRET) imaging offers a unique method to robustly quantify receptor-ligand engagement in live intact animals, which is critical to assess the delivery efficacy of therapeutics. However, to date, non-invasive imaging approaches that can simultaneously measure cellular drug delivery efficacy and metabolic response are lacking. A major challenge for the implementation of concurrent optical and MFLI-FRET in vivo whole-body preclinical imaging is the spectral crowding and cross-contamination between fluorescent probes. Methods: We report on a strategy that relies on a dark quencher enabling simultaneous assessment of receptor-ligand engagement and tumor metabolism in intact live mice. Several optical imaging approaches, such as in vitro NIR FLI microscopy (FLIM) and in vivo wide-field MFLI, were used to validate a novel donor-dark quencher FRET pair. IRDye 800CW 2-deoxyglucose (2-DG) imaging was multiplexed with MFLI-FRET of NIR-labeled transferrin FRET pair (Tf-AF700/Tf-QC-1) to monitor tumor metabolism and probe uptake in breast tumor xenografts in intact live nude mice. Immunohistochemistry was used to validate in vivo imaging results. Results: First, we establish that IRDye QC-1 (QC-1) is an effective NIR dark acceptor for the FRET-induced quenching of donor Alexa Fluor 700 (AF700). Second, we report on simultaneous in vivo imaging of the metabolic probe 2-DG and MFLI-FRET imaging of Tf-AF700/Tf-QC-1 uptake in tumors. Such multiplexed imaging revealed an inverse relationship between 2-DG uptake and Tf intracellular delivery, suggesting that 2-DG signal may predict the efficacy of intracellular targeted delivery. Conclusions: Overall, our methodology enables for the first time simultaneous non-invasive monitoring of intracellular drug delivery and metabolic response in preclinical studies.
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Affiliation(s)
- Alena Rudkouskaya
- Department of Cellular and Molecular Physiology, Albany Medical College, Albany, NY 12208, USA
| | - Nattawut Sinsuebphon
- Center for Modeling, Simulation, and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Marien Ochoa
- Center for Modeling, Simulation, and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Sez-Jade Chen
- Center for Modeling, Simulation, and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Joseph E. Mazurkiewicz
- Department of Neuroscience and Experimental Therapeutics, Albany Medical College, Albany, NY 12208, USA
| | - Xavier Intes
- Center for Modeling, Simulation, and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Margarida Barroso
- Department of Cellular and Molecular Physiology, Albany Medical College, Albany, NY 12208, USA
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71
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Smith JT, Aguénounon E, Gioux S, Intes X. Macroscopic fluorescence lifetime topography enhanced via spatial frequency domain imaging. OPTICS LETTERS 2020; 45:4232-4235. [PMID: 32735266 PMCID: PMC7935427 DOI: 10.1364/ol.397605] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
We report on a macroscopic fluorescence lifetime imaging (MFLI) topography computational framework based around machine learning with the main goal of retrieving the depth of fluorescent inclusions deeply seated in bio-tissues. This approach leverages the depth-resolved information inherent to time-resolved fluorescence data sets coupled with the retrieval of in situ optical properties as obtained via spatial frequency domain imaging (SFDI). Specifically, a Siamese network architecture is proposed with optical properties (OPs) and time-resolved fluorescence decays as input followed by simultaneous retrieval of lifetime maps and depth profiles. We validate our approach using comprehensive in silico data sets as well as with a phantom experiment. Overall, our results demonstrate that our approach can retrieve the depth of fluorescence inclusions, especially when coupled with optical properties estimation, with high accuracy. We expect the presented computational approach to find great utility in applications such as optical-guided surgery.
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Affiliation(s)
- Jason T. Smith
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180, USA
| | - Enagnon Aguénounon
- University of Strasbourg, ICube Laboratory, 300 Boulevard Sebastien Brant, 67412 Illkirch, France
| | - Sylvain Gioux
- University of Strasbourg, ICube Laboratory, 300 Boulevard Sebastien Brant, 67412 Illkirch, France
| | - Xavier Intes
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180, USA
- Corresponding author:
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72
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Smith JT, Ochoa M, Intes X. UNMIX-ME: spectral and lifetime fluorescence unmixing via deep learning. BIOMEDICAL OPTICS EXPRESS 2020; 11:3857-3874. [PMID: 33014571 PMCID: PMC7510912 DOI: 10.1364/boe.391992] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 04/30/2020] [Accepted: 04/30/2020] [Indexed: 05/18/2023]
Abstract
Hyperspectral fluorescence lifetime imaging allows for the simultaneous acquisition of spectrally resolved temporal fluorescence emission decays. In turn, the acquired rich multidimensional data set enables simultaneous imaging of multiple fluorescent species for a comprehensive molecular assessment of biotissues. However, to enable quantitative imaging, inherent spectral overlap between the considered fluorescent probes and potential bleed-through must be considered. Such a task is performed via either spectral or lifetime unmixing, typically independently. Herein, we present "UNMIX-ME" (unmix multiple emissions), a deep learning-based fluorescence unmixing routine, capable of quantitative fluorophore unmixing by simultaneously using both spectral and temporal signatures. UNMIX-ME was trained and validated using an in silico framework replicating the data acquisition process of a compressive hyperspectral fluorescent lifetime imaging platform (HMFLI). It was benchmarked against a conventional LSQ method for tri and quadri-exponential simulated samples. Last, UNMIX-ME's potential was assessed for NIR FRET in vitro and in vivo preclinical applications.
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Affiliation(s)
- Jason T Smith
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
- These authors contributed equally
| | - Marien Ochoa
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
- These authors contributed equally
| | - Xavier Intes
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
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73
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DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning. Nat Methods 2020; 17:734-740. [PMID: 32541853 PMCID: PMC7610486 DOI: 10.1038/s41592-020-0853-5] [Citation(s) in RCA: 126] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Accepted: 05/06/2020] [Indexed: 12/24/2022]
Abstract
An outstanding challenge in single-molecule localization microscopy is the accurate and precise localization of individual point emitters in three dimensions in densely labeled samples. One established approach for three-dimensional single-molecule localization is point-spread-function (PSF) engineering, in which the PSF is engineered to vary distinctively with emitter depth using additional optical elements. However, images of dense emitters, which are desirable for improving temporal resolution, pose a challenge for algorithmic localization of engineered PSFs, due to lateral overlap of the emitter PSFs. Here we train a neural network to localize multiple emitters with densely overlapping Tetrapod PSFs over a large axial range. We then use the network to design the optimal PSF for the multi-emitter case. We demonstrate our approach experimentally with super-resolution reconstructions of mitochondria and volumetric imaging of fluorescently labeled telomeres in cells. Our approach, DeepSTORM3D, enables the study of biological processes in whole cells at timescales that are rarely explored in localization microscopy.
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74
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Datta R, Heaster TM, Sharick JT, Gillette AA, Skala MC. Fluorescence lifetime imaging microscopy: fundamentals and advances in instrumentation, analysis, and applications. JOURNAL OF BIOMEDICAL OPTICS 2020; 25:1-43. [PMID: 32406215 PMCID: PMC7219965 DOI: 10.1117/1.jbo.25.7.071203] [Citation(s) in RCA: 326] [Impact Index Per Article: 81.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 04/24/2020] [Indexed: 05/18/2023]
Abstract
SIGNIFICANCE Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique to distinguish the unique molecular environment of fluorophores. FLIM measures the time a fluorophore remains in an excited state before emitting a photon, and detects molecular variations of fluorophores that are not apparent with spectral techniques alone. FLIM is sensitive to multiple biomedical processes including disease progression and drug efficacy. AIM We provide an overview of FLIM principles, instrumentation, and analysis while highlighting the latest developments and biological applications. APPROACH This review covers FLIM principles and theory, including advantages over intensity-based fluorescence measurements. Fundamentals of FLIM instrumentation in time- and frequency-domains are summarized, along with recent developments. Image segmentation and analysis strategies that quantify spatial and molecular features of cellular heterogeneity are reviewed. Finally, representative applications are provided including high-resolution FLIM of cell- and organelle-level molecular changes, use of exogenous and endogenous fluorophores, and imaging protein-protein interactions with Förster resonance energy transfer (FRET). Advantages and limitations of FLIM are also discussed. CONCLUSIONS FLIM is advantageous for probing molecular environments of fluorophores to inform on fluorophore behavior that cannot be elucidated with intensity measurements alone. Development of FLIM technologies, analysis, and applications will further advance biological research and clinical assessments.
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Affiliation(s)
- Rupsa Datta
- Morgridge Institute for Research, Madison, Wisconsin, United States
| | - Tiffany M. Heaster
- Morgridge Institute for Research, Madison, Wisconsin, United States
- University of Wisconsin, Department of Biomedical Engineering, Madison, Wisconsin, United States
| | - Joe T. Sharick
- Morgridge Institute for Research, Madison, Wisconsin, United States
| | - Amani A. Gillette
- Morgridge Institute for Research, Madison, Wisconsin, United States
- University of Wisconsin, Department of Biomedical Engineering, Madison, Wisconsin, United States
| | - Melissa C. Skala
- Morgridge Institute for Research, Madison, Wisconsin, United States
- University of Wisconsin, Department of Biomedical Engineering, Madison, Wisconsin, United States
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75
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Okkelman IA, Neto N, Papkovsky DB, Monaghan MG, Dmitriev RI. A deeper understanding of intestinal organoid metabolism revealed by combining fluorescence lifetime imaging microscopy (FLIM) and extracellular flux analyses. Redox Biol 2020; 30:101420. [PMID: 31935648 PMCID: PMC6957829 DOI: 10.1016/j.redox.2019.101420] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 12/13/2019] [Accepted: 12/29/2019] [Indexed: 12/21/2022] Open
Abstract
Stem cells and the niche in which they reside feature a complex microenvironment with tightly regulated homeostasis, cell-cell interactions and dynamic regulation of metabolism. A significant number of organoid models has been described over the last decade, yet few methodologies can enable single cell level resolution analysis of the stem cell niche metabolic demands, in real-time and without perturbing integrity. Here, we studied the redox metabolism of Lgr5-GFP intestinal organoids by two emerging microscopy approaches based on luminescence lifetime measurement - fluorescence-based FLIM for NAD(P)H, and phosphorescence-based PLIM for real-time oxygenation. We found that exposure of stem (Lgr5-GFP) and differentiated (no GFP) cells to high and low glucose concentrations resulted in measurable shifts in oxygenation and redox status. NAD(P)H-FLIM and O2-PLIM both indicated that at high 'basal' glucose conditions, Lgr5-GFP cells had lower activity of oxidative phosphorylation when compared with cells lacking Lgr5. However, when exposed to low (0.5 mM) glucose, stem cells utilized oxidative metabolism more dynamically than non-stem cells. The high heterogeneity of complex 3D architecture and energy production pathways of Lgr5-GFP organoids were also confirmed by the extracellular flux (XF) analysis. Our data reveals that combined analysis of NAD(P)H-FLIM and organoid oxygenation by PLIM represents promising approach for studying stem cell niche metabolism in a live readout.
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Affiliation(s)
- Irina A Okkelman
- Laboratory of Biophysics and Bioanalysis, ABCRF, University College Cork, Cavanagh Pharmacy Building, College Road, Cork, T12 K8AF, Ireland
| | - Nuno Neto
- Department of Mechanical and Manufacturing Engineering, Trinity College Dublin, Ireland; Trinity Centre for Biomedical Engineering, Trinity College Dublin, Ireland
| | - Dmitri B Papkovsky
- Laboratory of Biophysics and Bioanalysis, ABCRF, University College Cork, Cavanagh Pharmacy Building, College Road, Cork, T12 K8AF, Ireland
| | - Michael G Monaghan
- Department of Mechanical and Manufacturing Engineering, Trinity College Dublin, Ireland; Trinity Centre for Biomedical Engineering, Trinity College Dublin, Ireland; Advanced Materials and BioEngineering Research (AMBER) Centre at Trinity College Dublin and Royal College of Surgeons in Ireland, Dublin, Ireland.
| | - Ruslan I Dmitriev
- Laboratory of Biophysics and Bioanalysis, ABCRF, University College Cork, Cavanagh Pharmacy Building, College Road, Cork, T12 K8AF, Ireland; Institute for Regenerative Medicine, I.M. Sechenov First Moscow State University, Moscow, Russian Federation.
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