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Nguyen TD, Chen YI, Chen LH, Yeh HC. Recent Advances in Single-Molecule Tracking and Imaging Techniques. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2023; 16:253-284. [PMID: 37314878 DOI: 10.1146/annurev-anchem-091922-073057] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Since the early 1990s, single-molecule detection in solution at room temperature has enabled direct observation of single biomolecules at work in real time and under physiological conditions, providing insights into complex biological systems that the traditional ensemble methods cannot offer. In particular, recent advances in single-molecule tracking techniques allow researchers to follow individual biomolecules in their native environments for a timescale of seconds to minutes, revealing not only the distinct pathways these biomolecules take for downstream signaling but also their roles in supporting life. In this review, we discuss various single-molecule tracking and imaging techniques developed to date, with an emphasis on advanced three-dimensional (3D) tracking systems that not only achieve ultrahigh spatiotemporal resolution but also provide sufficient working depths suitable for tracking single molecules in 3D tissue models. We then summarize the observables that can be extracted from the trajectory data. Methods to perform single-molecule clustering analysis and future directions are also discussed.
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Affiliation(s)
- Trung Duc Nguyen
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas, USA;
| | - Yuan-I Chen
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas, USA;
| | - Limin H Chen
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas, USA;
| | - Hsin-Chih Yeh
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas, USA;
- Texas Materials Institute, University of Texas at Austin, Austin, Texas, USA
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Chen YI, Chang YJ, Liao SC, Nguyen TD, Yang J, Kuo YA, Hong S, Liu YL, Rylander HG, Santacruz SR, Yankeelov TE, Yeh HC. Generative adversarial network enables rapid and robust fluorescence lifetime image analysis in live cells. Commun Biol 2022; 5:18. [PMID: 35017629 PMCID: PMC8752789 DOI: 10.1038/s42003-021-02938-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 11/24/2021] [Indexed: 11/09/2022] Open
Abstract
Fluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study molecular states in complex cellular environment as the lifetime readings are not biased by fluorophore concentration or excitation power. However, the current methods to generate FLIM images are either computationally intensive or unreliable when the number of photons acquired at each pixel is low. Here we introduce a new deep learning-based method termed flimGANE (fluorescence lifetime imaging based on Generative Adversarial Network Estimation) that can rapidly generate accurate and high-quality FLIM images even in the photon-starved conditions. We demonstrated our model is up to 2,800 times faster than the gold standard time-domain maximum likelihood estimation (TD_MLE) and that flimGANE provides a more accurate analysis of low-photon-count histograms in barcode identification, cellular structure visualization, Förster resonance energy transfer characterization, and metabolic state analysis in live cells. With its advantages in speed and reliability, flimGANE is particularly useful in fundamental biological research and clinical applications, where high-speed analysis is critical.
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Affiliation(s)
- Yuan-I Chen
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Yin-Jui Chang
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Shih-Chu Liao
- ISS, Inc., 1602 Newton Drive, Champaign, IL, 61822, USA
| | - Trung Duc Nguyen
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Jianchen Yang
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Yu-An Kuo
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Soonwoo Hong
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Yen-Liang Liu
- Master Program for Biomedical Engineering, China Medical University, Taichung, 406040, Taiwan
- Research Center for Cancer Biology, China Medical University, Taichung, 406040, Taiwan
| | - H Grady Rylander
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Samantha R Santacruz
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
- Institute for Neuroscience, The University of Texas at Austin, Austin, TX, 78712, USA
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Thomas E Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, 78712, USA
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX, 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, 78712, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Hsin-Chih Yeh
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA.
- Texas Materials Institute, The University of Texas at Austin, Austin, TX, 78712, USA.
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