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      Label-free metabolic and structural profiling of dynamic biological samples using multimodal optical microscopy with sensorless adaptive optics

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          Abstract

          Label-free optical microscopy has matured as a noninvasive tool for biological imaging; yet, it is criticized for its lack of specificity, slow acquisition and processing times, and weak and noisy optical signals that lead to inaccuracies in quantification. We introduce FOCALS (Fast Optical Coherence, Autofluorescence Lifetime imaging, and Second harmonic generation) microscopy capable of generating NAD(P)H fluorescence lifetime, second harmonic generation (SHG), and polarization-sensitive optical coherence microscopy (OCM) images simultaneously. Multimodal imaging generates quantitative metabolic and morphological profiles of biological samples in vitro, ex vivo, and in vivo. Fast analog detection of fluorescence lifetime and real-time processing on a graphical processing unit enables longitudinal imaging of biological dynamics. We detail the effect of optical aberrations on the accuracy of FLIM beyond the context of undistorting image features. To compensate for the sample-induced aberrations, we implemented a closed-loop single-shot sensorless adaptive optics solution, which uses computational adaptive optics of OCM for wavefront estimation within 2 s and improves the quality of quantitative fluorescence imaging in thick tissues. Multimodal imaging with complementary contrasts improves the specificity and enables multidimensional quantification of the optical signatures in vitro, ex vivo, and in vivo, fast acquisition and real-time processing improve imaging speed by 4–40 × while maintaining enough signal for quantitative nonlinear microscopy, and adaptive optics improves the overall versatility, which enable FOCALS microscopy to overcome the limits of traditional label-free imaging techniques.

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          Collagen density promotes mammary tumor initiation and progression

          Background Mammographically dense breast tissue is one of the greatest risk factors for developing breast carcinoma. Despite the strong clinical correlation, breast density has not been causally linked to tumorigenesis, largely because no animal model has existed for studying breast tissue density. Importantly, regions of high breast density are associated with increased stromal collagen. Thus, the influence of the extracellular matrix on breast carcinoma development and the underlying molecular mechanisms are not understood. Methods To study the effects of collagen density on mammary tumor formation and progression, we utilized a bi-transgenic tumor model with increased stromal collagen in mouse mammary tissue. Imaging of the tumors and tumor-stromal interface in live tumor tissue was performed with multiphoton laser-scanning microscopy to generate multiphoton excitation and spectrally resolved fluorescent lifetimes of endogenous fluorophores. Second harmonic generation was utilized to image stromal collagen. Results Herein we demonstrate that increased stromal collagen in mouse mammary tissue significantly increases tumor formation approximately three-fold (p < 0.00001) and results in a significantly more invasive phenotype with approximately three times more lung metastasis (p < 0.05). Furthermore, the increased invasive phenotype of tumor cells that arose within collagen-dense mammary tissues remains after tumor explants are cultured within reconstituted three-dimensional collagen gels. To better understand this behavior we imaged live tumors using nonlinear optical imaging approaches to demonstrate that local invasion is facilitated by stromal collagen re-organization and that this behavior is significantly increased in collagen-dense tissues. In addition, using multiphoton fluorescence and spectral lifetime imaging we identify a metabolic signature for flavin adenine dinucleotide, with increased fluorescent intensity and lifetime, in invading metastatic cells. Conclusion This study provides the first data causally linking increased stromal collagen to mammary tumor formation and metastasis, and demonstrates that fundamental differences arise and persist in epithelial tumor cells that progressed within collagen-dense microenvironments. Furthermore, the imaging techniques and signature identified in this work may provide useful diagnostic tools to rapidly assess fresh tissue biopsies.
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            Interpreting second-harmonic generation images of collagen I fibrils.

            Fibrillar collagen, being highly noncentrosymmetric, possesses a tremendous nonlinear susceptibility. As a result, second-harmonic generation (SHG) microscopy of collagen produces extremely bright and robust signals, providing an invaluable tool for imaging tissue structure with submicron resolution. Here we discuss fundamental principles governing SHG phase matching with the tightly focusing optics used in microscopy. Their application to collagen imaging yields several biophysical features characteristic of native collagen structure: SHG radiates from the shell of a collagen fibril, rather than from its bulk. This SHG shell may correspond to the supporting element of the fibril. Physiologically relevant changes in solution ionic strength alter the ratio of forward-to-backward propagating SHG, implying a resulting change in the SHG shell thickness. Fibrillogenesis can be resolved in immature tissue by directly imaging backward-propagating SHG. Such findings are crucial to the design and development of forthcoming diagnostic and research tools.
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              Fluorescence lifetime imaging microscopy: fundamentals and advances in instrumentation, analysis, and applications

              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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                Author and article information

                Contributors
                boppart@illinois.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                2 March 2022
                2 March 2022
                2022
                : 12
                : 3438
                Affiliations
                [1 ]GRID grid.35403.31, ISNI 0000 0004 1936 9991, Beckman Institute for Advanced Science and Technology, , University of Illinois at Urbana-Champaign, ; Urbana, USA
                [2 ]GRID grid.35403.31, ISNI 0000 0004 1936 9991, Department of Electrical and Computer Engineering, , University of Illinois at Urbana-Champaign, ; Urbana, USA
                [3 ]GRID grid.35403.31, ISNI 0000 0004 1936 9991, Department of Bioengineering, , University of Illinois at Urbana-Champaign, ; Urbana, USA
                [4 ]GRID grid.35403.31, ISNI 0000 0004 1936 9991, The School of Molecular and Cellular Biology, , University of Illinois at Urbana-Champaign, ; Urbana, USA
                [5 ]GRID grid.35403.31, ISNI 0000 0004 1936 9991, Carle Illinois College of Medicine, , University of Illinois at Urbana-Champaign, ; Urbana, USA
                [6 ]GRID grid.35403.31, ISNI 0000 0004 1936 9991, Cancer Center at Illinois, , University of Illinois at Urbana-Champaign, ; Urbana, USA
                Article
                6926
                10.1038/s41598-022-06926-w
                8891278
                35236862
                57f465d8-6de4-4760-bf9a-5bc86633b4b0
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 8 September 2021
                : 1 February 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: DGE-1746047
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: T32-ES007326
                Award ID: R01CA213149
                Award ID: R01EB023232
                Award ID: R01CA241618
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000181, Air Force Office of Scientific Research;
                Award ID: FA9550-17-1-0387
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                biomedical engineering,adaptive optics,imaging and sensing,interference microscopy,multiphoton microscopy,optical imaging

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