1
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Zheng R, Wu R, Liu Y, Sun Z, Xue Z, Bagheri Y, Khajouei S, Mi L, Tian Q, Pho R, Liu Q, Siddiqui S, Ren K, You M. Multiplexed sequential imaging in living cells with orthogonal fluorogenic RNA aptamer/dye pairs. Nucleic Acids Res 2024; 52:e67. [PMID: 38922685 PMCID: PMC11347136 DOI: 10.1093/nar/gkae551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 06/01/2024] [Accepted: 06/14/2024] [Indexed: 06/28/2024] Open
Abstract
Detecting multiple targets in living cells is important in cell biology. However, multiplexed fluorescence imaging beyond two-to-three targets remains a technical challenge. Herein, we introduce a multiplexed imaging strategy, 'sequential Fluorogenic RNA Imaging-Enabled Sensor' (seqFRIES), which enables live-cell target detection via sequential rounds of imaging-and-stripping. In seqFRIES, multiple orthogonal fluorogenic RNA aptamers are genetically encoded inside cells, and then the corresponding cell membrane permeable dye molecules are added, imaged, and rapidly removed in consecutive detection cycles. As a proof-of-concept, we have identified in this study four fluorogenic RNA aptamer/dye pairs that can be used for highly orthogonal and multiplexed imaging in living bacterial and mammalian cells. After further optimizing the cellular fluorescence activation and deactivation kinetics of these RNA/dye pairs, the whole four-color semi-quantitative seqFRIES process can be completed in ∼20 min. Meanwhile, seqFRIES-mediated simultaneous detection of critical signalling molecules and mRNA targets was also achieved within individual living cells. We expect our validation of this new seqFRIES concept here will facilitate the further development and potential broad usage of these orthogonal fluorogenic RNA/dye pairs for multiplexed and dynamic live-cell imaging and cell biology studies.
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Affiliation(s)
- Ru Zheng
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
| | - Rigumula Wu
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
| | - Yuanchang Liu
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
| | - Zhining Sun
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
| | - Zhaolin Xue
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
| | - Yousef Bagheri
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
| | - Sima Khajouei
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
| | - Lan Mi
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
| | - Qian Tian
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
| | - Raymond Pho
- Department of Chemical Engineering, University of Massachusetts, Amherst, MA 01003, USA
| | - Qinge Liu
- Department of Chemistry, Mount Holyoke College, Holyoke, MA 01075, USA
| | - Sidrat Siddiqui
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
| | - Kewei Ren
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
- School of Chemistry and Chemical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Mingxu You
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
- Molecular and Cellular Biology Program, University of Massachusetts, Amherst, MA 01003, USA
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2
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Kshirsagar A, Politza AJ, Guan W. Deep Learning Enabled Universal Multiplexed Fluorescence Detection for Point-of-Care Applications. ACS Sens 2024; 9:4017-4027. [PMID: 39010300 DOI: 10.1021/acssensors.4c00860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
There is a significant demand for multiplexed fluorescence sensing and detection across a range of applications. Yet, the development of portable and compact multiplexable systems remains a substantial challenge. This difficulty largely stems from the inherent need for spectrum separation, which typically requires sophisticated and expensive optical components. Here, we demonstrate a compact, lens-free, and cost-effective fluorescence sensing setup that incorporates machine learning for scalable multiplexed fluorescence detection. This method utilizes low-cost optical components and a pretrained machine learning (ML) model to enable multiplexed fluorescence sensing without optical adjustments. Its multiplexing capability can be easily scaled up through updates to the machine learning model without altering the hardware. We demonstrate its real-world application in a probe-based multiplexed Loop-Mediated Isothermal Amplification (LAMP) assay designed to simultaneously detect three common respiratory viruses within a single reaction. The effectiveness of this approach highlights the system's potential for point-of-care applications that require cost-effective and scalable solutions. The machine learning-enabled multiplexed fluorescence sensing demonstrated in this work would pave the way for widespread adoption in diverse settings, from clinical laboratories to field diagnostics.
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Affiliation(s)
- Aneesh Kshirsagar
- Department of Electrical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Anthony J Politza
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Weihua Guan
- Department of Electrical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
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3
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Hwang W, Raymond T, McPartland T, Jeong S, Evans CL. Fluorescence Lifetime Multiplexing (FLEX) for simultaneous high dimensional spatial biology in 3D. Commun Biol 2024; 7:1012. [PMID: 39154126 PMCID: PMC11330493 DOI: 10.1038/s42003-024-06702-8] [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: 02/01/2024] [Accepted: 08/08/2024] [Indexed: 08/19/2024] Open
Abstract
Immunohistochemistry is a crucial method for detecting specific proteins within tissue samples, yet constrained to one biomarker per tissue section. Multiplexed immunofluorescence, while allowing simultaneous visualization of multiple proteins, faces limitations in the number of simultaneous fluorescent labels due to spectral overlap. Although cyclic immunofluorescence techniques have successfully broadened antibody staining capacities in a single tissue sample, they are plagued by time-consuming and labor-intensive procedures, sample degradation risks, and inability to scale beyond thin sections. In this study, we introduce the use of 3D confocal Fluorescence Lifetime Imaging Microscopy as a high-throughput, multiplexed immunofluorescence platform that can differentiate 11 or more biomarkers in 3D tissue volumes. Leveraging both spectral and lifetime information, this approach allows for practical spatial biology in thin sections that can readily scale to larger volumes of tissue. We believe that this highly multiplexed and versatile biomarker imaging platform will significantly expedite cancer research and enable new translational approaches in the future.
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Affiliation(s)
- Wonsang Hwang
- Wellman Center for Photomedicine, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Tucker Raymond
- Wellman Center for Photomedicine, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Tyler McPartland
- Wellman Center for Photomedicine, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | | | - Conor L Evans
- Wellman Center for Photomedicine, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA.
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4
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Han X, Lu X, Li PH, Wang S, Schalek R, Meirovitch Y, Lin Z, Adhinarta J, Murray KD, MacNiven LM, Berger DR, Wu Y, Fang T, Meral ES, Asraf S, Ploegh H, Pfister H, Wei D, Jain V, Trimmer JS, Lichtman JW. Multiplexed volumetric CLEM enabled by scFvs provides insights into the cytology of cerebellar cortex. Nat Commun 2024; 15:6648. [PMID: 39103318 DOI: 10.1038/s41467-024-50411-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 07/01/2024] [Indexed: 08/07/2024] Open
Abstract
Mapping neuronal networks is a central focus in neuroscience. While volume electron microscopy (vEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide molecular information to identify cell types or functions. We developed an approach that uses fluorescent single-chain variable fragments (scFvs) to perform multiplexed detergent-free immunolabeling and volumetric-correlated-light-and-electron-microscopy on the same sample. We generated eight fluorescent scFvs targeting brain markers. Six fluorescent probes were imaged in the cerebellum of a female mouse, using confocal microscopy with spectral unmixing, followed by vEM of the same sample. The results provide excellent ultrastructure superimposed with multiple fluorescence channels. Using this approach, we documented a poorly described cell type, two types of mossy fiber terminals, and the subcellular localization of one type of ion channel. Because scFvs can be derived from existing monoclonal antibodies, hundreds of such probes can be generated to enable molecular overlays for connectomic studies.
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Affiliation(s)
- Xiaomeng Han
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA.
| | - Xiaotang Lu
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA.
| | | | - Shuohong Wang
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Richard Schalek
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Yaron Meirovitch
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Zudi Lin
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Jason Adhinarta
- Computer Science Department, Boston College, Chestnut Hill, MA, USA
| | - Karl D Murray
- Department of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, CA, USA
| | - Leah M MacNiven
- Department of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, CA, USA
| | - Daniel R Berger
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Yuelong Wu
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Tao Fang
- Program of Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, USA
| | | | - Shadnan Asraf
- School of Public Health, University of Massachusetts Amherst, Amherst, MA, USA
| | - Hidde Ploegh
- Program of Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, USA
| | - Hanspeter Pfister
- Department of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, CA, USA
| | - Donglai Wei
- Computer Science Department, Boston College, Chestnut Hill, MA, USA
| | | | - James S Trimmer
- Department of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, CA, USA
| | - Jeff W Lichtman
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA.
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5
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Campbell KR, Hallada LP, Huang YS, Solecki DJ. From Blur to Brilliance: The Ascendance of Advanced Microscopy in Neuronal Cell Biology. Annu Rev Neurosci 2024; 47:235-253. [PMID: 38608643 DOI: 10.1146/annurev-neuro-111020-090208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/14/2024]
Abstract
The intricate network of the brain's neurons and synapses poses unparalleled challenges for research, distinct from other biological studies. This is particularly true when dissecting how neurons and their functional units work at a cell biological level. While traditional microscopy has been foundational, it was unable to reveal the deeper complexities of neural interactions. However, an imaging renaissance has transformed our capabilities. Advancements in light and electron microscopy, combined with correlative imaging, now achieve unprecedented resolutions, uncovering the most nuanced neural structures. Maximizing these tools requires more than just technical proficiency. It is crucial to align research aims, allocate resources wisely, and analyze data effectively. At the heart of this evolution is interdisciplinary collaboration, where various experts come together to translate detailed imagery into significant biological insights. This review navigates the latest developments in microscopy, underscoring both the promise of and prerequisites for bending this powerful tool set to understanding neuronal cell biology.
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Affiliation(s)
- Kirby R Campbell
- Neuronal Cell Biology Division, Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - Liam P Hallada
- St. Jude Graduate School of Biomedical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Neuronal Cell Biology Division, Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - Yu-Shan Huang
- Neuronal Cell Biology Division, Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - David J Solecki
- Neuronal Cell Biology Division, Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
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6
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Iyer RR, Applegate CC, Arogundade OH, Bangru S, Berg IC, Emon B, Porras-Gomez M, Hsieh PH, Jeong Y, Kim Y, Knox HJ, Moghaddam AO, Renteria CA, Richard C, Santaliz-Casiano A, Sengupta S, Wang J, Zambuto SG, Zeballos MA, Pool M, Bhargava R, Gaskins HR. Inspiring a convergent engineering approach to measure and model the tissue microenvironment. Heliyon 2024; 10:e32546. [PMID: 38975228 PMCID: PMC11226808 DOI: 10.1016/j.heliyon.2024.e32546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 05/22/2024] [Accepted: 06/05/2024] [Indexed: 07/09/2024] Open
Abstract
Understanding the molecular and physical complexity of the tissue microenvironment (TiME) in the context of its spatiotemporal organization has remained an enduring challenge. Recent advances in engineering and data science are now promising the ability to study the structure, functions, and dynamics of the TiME in unprecedented detail; however, many advances still occur in silos that rarely integrate information to study the TiME in its full detail. This review provides an integrative overview of the engineering principles underlying chemical, optical, electrical, mechanical, and computational science to probe, sense, model, and fabricate the TiME. In individual sections, we first summarize the underlying principles, capabilities, and scope of emerging technologies, the breakthrough discoveries enabled by each technology and recent, promising innovations. We provide perspectives on the potential of these advances in answering critical questions about the TiME and its role in various disease and developmental processes. Finally, we present an integrative view that appreciates the major scientific and educational aspects in the study of the TiME.
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Affiliation(s)
- Rishyashring R. Iyer
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Catherine C. Applegate
- Division of Nutritional Sciences, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Opeyemi H. Arogundade
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Sushant Bangru
- Department of Biochemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Ian C. Berg
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Bashar Emon
- Department of Mechanical Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Marilyn Porras-Gomez
- Department of Materials Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Pei-Hsuan Hsieh
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Yoon Jeong
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Yongdeok Kim
- Department of Materials Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Hailey J. Knox
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Amir Ostadi Moghaddam
- Department of Mechanical Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Carlos A. Renteria
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Craig Richard
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Ashlie Santaliz-Casiano
- Division of Nutritional Sciences, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Sourya Sengupta
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Jason Wang
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Samantha G. Zambuto
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Maria A. Zeballos
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Marcia Pool
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Rohit Bhargava
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Mechanical Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Chemical and Biochemical Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- NIH/NIBIB P41 Center for Label-free Imaging and Multiscale Biophotonics (CLIMB), University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - H. Rex Gaskins
- Division of Nutritional Sciences, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Animal Sciences, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Biomedical and Translational Sciences, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Pathobiology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
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7
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Bartkova S, Zapotoczna M, Sanka I, Scheler O. A Guide to Biodetection in Droplets. Anal Chem 2024; 96:9745-9755. [PMID: 38842026 PMCID: PMC11190884 DOI: 10.1021/acs.analchem.3c04282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 05/21/2024] [Accepted: 05/22/2024] [Indexed: 06/07/2024]
Abstract
Droplet-based methods for optical biodetection enable unprecedented high-throughput experimental parameters. The methods, however, remain underused due to the accompanying multidisciplinary and complicated experimental workflows. Here, we provide a tutorial for droplet-based optical biodetection workflows with a focus on the key aspect of label selection. By discussing and guiding readers through recent state-of-the-art studies, we aim to make droplet-based approaches more accessible to the general scientific public.
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Affiliation(s)
- Simona Bartkova
- Department
of Chemistry and Biotechnology, Tallinn
University of Technology (TalTech), Akadeemia tee 15, Tallinn 12618, Estonia
| | - Marta Zapotoczna
- Faculty
of Biology, Biological and Chemical Research Centre, University of Warsaw, Żwirki i Wigury 101, 02-089 Warsaw, Poland
| | - Immanuel Sanka
- Department
of Chemistry and Biotechnology, Tallinn
University of Technology (TalTech), Akadeemia tee 15, Tallinn 12618, Estonia
| | - Ott Scheler
- Department
of Chemistry and Biotechnology, Tallinn
University of Technology (TalTech), Akadeemia tee 15, Tallinn 12618, Estonia
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8
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Liu G, Ahn SW, Kang JW, Bhagavatula S, Matthew D, Martin S, Marlin C, So PTC, Tearney GJ, Jonas O. Two-site microendoscopic imaging probe for simultaneous three-dimensional imaging at two anatomic locations in tissues. OPTICS LETTERS 2024; 49:3312-3315. [PMID: 38875608 PMCID: PMC11298057 DOI: 10.1364/ol.525945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 05/16/2024] [Indexed: 06/16/2024]
Abstract
Systems that can image in three dimensions at cellular resolution and across different locations within an organism may enable insights into complex biological processes, such as immune responses, for which a single location measurement may be insufficient. In this Letter, we describe an in vivo two-site imaging probe (TIP) that can simultaneously image two anatomic sites with a maximum separation of a few centimeters. The TIP consists of two identical bendable graded index (GRIN) lenses and is demonstrated by a two-photon two-color fluorescence imaging system. Each GRIN lens has a field of view of 162 × 162 × 170 µm3, a nominal numerical aperture of 0.5, a magnification of 0.7, and working distances of 0.2 mm in air for both ends. A blind linear unmixing algorithm is applied to suppress bleedthrough between channels. We use this system to successfully demonstrate two-site two-photon two-color imaging of two biomedically relevant samples, i.e., (1) a mixture of two autofluorescent anti-cancer drugs and (2) a live hybrid tumor consisting of two spectrally distinct fluorescent cell lines.
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Affiliation(s)
- Guigen Liu
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Sebastian W. Ahn
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Jeon Woong Kang
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Sharath Bhagavatula
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Destiny Matthew
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Samantha Martin
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Courtney Marlin
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Peter T. C. So
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Guillermo J. Tearney
- Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114, USA
| | - Oliver Jonas
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
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9
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Shroff H, Testa I, Jug F, Manley S. Live-cell imaging powered by computation. Nat Rev Mol Cell Biol 2024; 25:443-463. [PMID: 38378991 DOI: 10.1038/s41580-024-00702-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/10/2024] [Indexed: 02/22/2024]
Abstract
The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preserving cell viability. Computational methods can help to address this challenge and are now shifting the boundaries of what is possible to capture in living systems. In this Review, we discuss these computational methods focusing on artificial intelligence-based approaches that can be layered on top of commonly used existing microscopies as well as hybrid methods that integrate computation and microscope hardware. We specifically discuss how computational approaches can improve the signal-to-noise ratio, spatial resolution, temporal resolution and multi-colour capacity of live-cell imaging.
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Affiliation(s)
- Hari Shroff
- Janelia Research Campus, Howard Hughes Medical Institute (HHMI), Ashburn, VA, USA
| | - Ilaria Testa
- Department of Applied Physics and Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Florian Jug
- Fondazione Human Technopole (HT), Milan, Italy
| | - Suliana Manley
- Institute of Physics, School of Basic Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland.
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10
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Tsuchida A, Kaneko T, Nishikawa K, Kawasaki M, Yokokawa R, Shintaku H. Opto-combinatorial indexing enables high-content transcriptomics by linking cell images and transcriptomes. LAB ON A CHIP 2024; 24:2287-2297. [PMID: 38506394 DOI: 10.1039/d3lc00866e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
We introduce a simple integrated analysis method that links cellular phenotypic behaviour with single-cell RNA sequencing (scRNA-seq) by utilizing a combination of optical indices from cells and hydrogel beads. With our method, the combinations, referred to as joint colour codes, enable the link via matching the optical combinations measured by conventional epi-fluorescence microscopy with the concatenated DNA molecular barcodes created by cell-hydrogel bead pairs and sequenced by next-generation sequencing. We validated our approach by demonstrating an accurate link between the cell image and scRNA-seq with mixed species experiments, longitudinal cell tagging by electroporation and lipofection, and gene expression analysis. Furthermore, we extended our approach to multiplexed chemical transcriptomics, which enabled us to identify distinct phenotypic behaviours in HeLa cells treated with various concentrations of paclitaxel, and determine the corresponding gene regulation associated with the formation of a multipolar spindle.
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Affiliation(s)
- Arata Tsuchida
- Cluster for Pioneering Research, RIKEN, Main Research Building 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
- Department of Micro Engineering, Graduate School of Engineering, Kyoto University, Kyotodaigaku-katsura, Nishikyo-ku, Kyoto 615-8540, Japan.
| | - Taikopaul Kaneko
- Cluster for Pioneering Research, RIKEN, Main Research Building 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Kaori Nishikawa
- Cluster for Pioneering Research, RIKEN, Main Research Building 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Mayu Kawasaki
- Cluster for Pioneering Research, RIKEN, Main Research Building 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Ryuji Yokokawa
- Department of Micro Engineering, Graduate School of Engineering, Kyoto University, Kyotodaigaku-katsura, Nishikyo-ku, Kyoto 615-8540, Japan.
| | - Hirofumi Shintaku
- Cluster for Pioneering Research, RIKEN, Main Research Building 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
- Department of Micro Engineering, Graduate School of Engineering, Kyoto University, Kyotodaigaku-katsura, Nishikyo-ku, Kyoto 615-8540, Japan.
- Institute for Life and Medical Science, Kyoto University, 53 Kawara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan
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11
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Valenta H, Bierbuesse F, Vitale R, Ruckebusch C, Vandenberg W, Dedecker P. Per-pixel unmixing of spectrally overlapping fluorophores using intra-exposure excitation modulation. Talanta 2024; 269:125397. [PMID: 38048682 DOI: 10.1016/j.talanta.2023.125397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/01/2023] [Accepted: 11/03/2023] [Indexed: 12/06/2023]
Abstract
Multilabel fluorescence imaging is essential for the visualization of complex systems, though a major challenge is the limited width of the useable spectral window. Here, we present a new method, exNEEMO, that enables per-pixel quantification of spectrally-overlapping fluorophores based on their light-induced dynamics, in a way that is compatible with a very broad range of timescales over which these dynamics may occur. Our approach makes use of intra-exposure modulation of the excitation light to distinguish the different emitters given their reference responses to this modulation. We use the approach to simultaneously image four green photochromic fluorescent proteins at the full spatial resolution of the imaging.
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12
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Martínez-Torres D, Maldonado V, Pérez-Gallardo C, Yañez R, Candia V, Kalaidzidis Y, Zerial M, Morales-Navarrete H, Segovia-Miranda F. Phenotypic characterization of liver tissue heterogeneity through a next-generation 3D single-cell atlas. Sci Rep 2024; 14:2823. [PMID: 38307948 PMCID: PMC10837128 DOI: 10.1038/s41598-024-53309-4] [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: 08/26/2023] [Accepted: 01/30/2024] [Indexed: 02/04/2024] Open
Abstract
Three-dimensional (3D) geometrical models are potent tools for quantifying complex tissue features and exploring structure-function relationships. However, these models are generally incomplete due to experimental limitations in acquiring multiple (> 4) fluorescent channels in thick tissue sections simultaneously. Indeed, predictive geometrical and functional models of the liver have been restricted to few tissue and cellular components, excluding important cellular populations such as hepatic stellate cells (HSCs) and Kupffer cells (KCs). Here, we combined deep-tissue immunostaining, multiphoton microscopy, deep-learning techniques, and 3D image processing to computationally expand the number of simultaneously reconstructed tissue structures. We then generated a spatial single-cell atlas of hepatic architecture (Hep3D), including all main tissue and cellular components at different stages of post-natal development in mice. We used Hep3D to quantitatively study 1) hepatic morphodynamics from early post-natal development to adulthood, and 2) the effect on the liver's overall structure when changing the hepatic environment after removing KCs. In addition to a complete description of bile canaliculi and sinusoidal network remodeling, our analysis uncovered unexpected spatiotemporal patterns of non-parenchymal cells and hepatocytes differing in size, number of nuclei, and DNA content. Surprisingly, we found that the specific depletion of KCs results in morphological changes in hepatocytes and HSCs. These findings reveal novel characteristics of liver heterogeneity and have important implications for both the structural organization of liver tissue and its function. Our next-gen 3D single-cell atlas is a powerful tool to understand liver tissue architecture, opening up avenues for in-depth investigations into tissue structure across both normal and pathological conditions.
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Affiliation(s)
- Dilan Martínez-Torres
- Department of Cell Biology, Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile
- Grupo de Procesos en Biología del Desarrollo (GDeP), Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile
| | - Valentina Maldonado
- Department of Cell Biology, Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile
- Grupo de Procesos en Biología del Desarrollo (GDeP), Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile
| | - Cristian Pérez-Gallardo
- Department of Cell Biology, Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile
- Grupo de Procesos en Biología del Desarrollo (GDeP), Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile
| | - Rodrigo Yañez
- Department of Cell Biology, Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile
- Grupo de Procesos en Biología del Desarrollo (GDeP), Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile
| | - Valeria Candia
- Department of Cell Biology, Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile
- Grupo de Procesos en Biología del Desarrollo (GDeP), Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile
| | - Yannis Kalaidzidis
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Marino Zerial
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Hernán Morales-Navarrete
- Department of Systems Biology of Development, University of Konstanz, Konstanz, Germany.
- Facultad de Ciencias Técnicas, Universidad Internacional Del Ecuador UIDE, Quito, Ecuador.
| | - Fabián Segovia-Miranda
- Department of Cell Biology, Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile.
- Grupo de Procesos en Biología del Desarrollo (GDeP), Faculty of Biological Sciences, Universidad de Concepción, Concepción, Chile.
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13
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Huang X, Gao X, Fu L. BINGO: a blind unmixing algorithm for ultra-multiplexing fluorescence images. Bioinformatics 2024; 40:btae052. [PMID: 38291952 PMCID: PMC10873573 DOI: 10.1093/bioinformatics/btae052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 02/15/2024] [Accepted: 02/15/2024] [Indexed: 02/01/2024] Open
Abstract
MOTIVATION Spectral imaging is often used to observe different objects with multiple fluorescent labels to reveal the development of the biological event. As the number of observed objects increases, the spectral overlap between fluorophores becomes more serious, and obtaining a "pure" picture of each fluorophore becomes a major challenge. Here, we propose a blind spectral unmixing algorithm called BINGO (Blind unmixing via SVD-based Initialization Nmf with project Gradient descent and spare cOnstrain), which can extract all kinds of fluorophores more accurately from highly overlapping multichannel data, even if the spectra of the fluorophores are extremely similar or their fluorescence intensity varies greatly. RESULTS BINGO can isolate up to 10 fluorophores from spectral imaging data for a single excitation. nine-color living HeLa cells were visualized distinctly with BINGO. It provides an important algorithmic tool for multiplex imaging studies, especially in intravital imaging. BINGO shows great potential in multicolor imaging for biomedical sciences. AVAILABILITY AND IMPLEMENTATION The source code used for this paper is available with the test data at https://github.com/Xinyuan555/BINGO_unmixing.
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Affiliation(s)
- Xinyuan Huang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
- Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Xiujuan Gao
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
- Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Ling Fu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
- Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan 430074, China
- School of Biomedical Engineering, Hainan University, Haikou 570228, China
- School of Physics and Optoelectronics Engineering, Hainan University, Haikou 570228, China
- Optics Valley Laboratory, Wuhan 430074, China
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14
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Cang H, Liu Y, Xing J. Mosaic-PICASSO: accurate crosstalk removal for multiplex fluorescence imaging. Bioinformatics 2024; 40:btad784. [PMID: 38175779 PMCID: PMC10781941 DOI: 10.1093/bioinformatics/btad784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 11/26/2023] [Accepted: 12/28/2023] [Indexed: 01/06/2024] Open
Abstract
MOTIVATION Ultra-multiplexed fluorescence imaging has revolutionized our understanding of biological systems, enabling the simultaneous visualization and quantification of multiple targets within biological specimens. A recent breakthrough in this field is PICASSO, a mutual-information-based technique capable of demixing up to 15 fluorophores without their spectra, thereby significantly simplifying the application of ultra-multiplexed fluorescence imaging. However, this study has identified a limitation of mutual information (MI)-based techniques. They do not differentiate between spatial colocalization and spectral mixing. Consequently, MI-based demixing may incorrectly interpret spatially co-localized targets as non-colocalized, leading to overcorrection. RESULTS We found that selecting regions within a multiplex image with low-spatial similarity for measuring spectroscopic mixing results in more accurate demixing. This method effectively minimizes overcorrections and promises to accelerate the broader adoption of ultra-multiplex imaging. AVAILABILITY AND IMPLEMENTATION The codes are available at https://github.com/xing-lab-pitt/mosaic-picasso.
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Affiliation(s)
- Hu Cang
- San Diego, CA 92172-721234, United States
| | - Yang Liu
- Departments of Medicine and Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, United States
- University of Pittsburgh Hillman Cancer Center, Pittsburgh, PA 15213, United States
| | - Jianhua Xing
- University of Pittsburgh Hillman Cancer Center, Pittsburgh, PA 15213, United States
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15213, United States
- Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15213, United States
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15
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Blanc H, Kaddour G, David NB, Supatto W, Livet J, Beaurepaire E, Mahou P. Chromatically Corrected Multicolor Multiphoton Microscopy. ACS PHOTONICS 2023; 10:4104-4111. [PMID: 38145164 PMCID: PMC10739991 DOI: 10.1021/acsphotonics.3c01104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Indexed: 12/26/2023]
Abstract
Simultaneous imaging of multiple labels in tissues is key to studying complex biological processes. Although strategies for color multiphoton excitation have been established, chromatic aberration remains a major problem when multiple excitation wavelengths are used in a scanning microscope. Chromatic aberration introduces a spatial shift between the foci of beams of different wavelengths that varies across the field of view, severely degrading the performance of color imaging. In this work, we propose an adaptive correction strategy that solves this problem in two-beam microscopy techniques. Axial chromatic aberration is corrected by a refractive phase mask that introduces pure defocus into one beam, while lateral chromatic aberration is corrected by a piezoelectric mirror that dynamically compensates for lateral shifts during scanning. We show that this light-efficient approach allows seamless chromatic correction over the entire field of view of different multiphoton objectives without compromising spatial and temporal resolution and that the effective area for beam-mixing processes can be increased by more than 1 order of magnitude. We illustrate this approach with simultaneous three-color, two-photon imaging of developing zebrafish embryos and fixed Brainbow mouse brain slices over large areas. These results establish a robust and efficient method for chromatically corrected multiphoton imaging.
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Affiliation(s)
- Hugo Blanc
- Laboratoire
d’Optique et Biosciences, Ecole Polytechnique, CNRS,
INSERM, Institut Polytechnique de Paris, 91128 Palaiseau, France
| | - Gabriel Kaddour
- Sorbonne
Université, INSERM, CNRS, Institut
de la Vision, 75012 Paris, France
| | - Nicolas B. David
- Laboratoire
d’Optique et Biosciences, Ecole Polytechnique, CNRS,
INSERM, Institut Polytechnique de Paris, 91128 Palaiseau, France
| | - Willy Supatto
- Laboratoire
d’Optique et Biosciences, Ecole Polytechnique, CNRS,
INSERM, Institut Polytechnique de Paris, 91128 Palaiseau, France
| | - Jean Livet
- Sorbonne
Université, INSERM, CNRS, Institut
de la Vision, 75012 Paris, France
| | - Emmanuel Beaurepaire
- Laboratoire
d’Optique et Biosciences, Ecole Polytechnique, CNRS,
INSERM, Institut Polytechnique de Paris, 91128 Palaiseau, France
| | - Pierre Mahou
- Laboratoire
d’Optique et Biosciences, Ecole Polytechnique, CNRS,
INSERM, Institut Polytechnique de Paris, 91128 Palaiseau, France
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16
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Landeros C, Oh J, Weissleder R, Lee H. Deep Learning Pipeline for Automated Cell Profiling from Cyclic Imaging. RESEARCH SQUARE 2023:rs.3.rs-3745061. [PMID: 38196620 PMCID: PMC10775369 DOI: 10.21203/rs.3.rs-3745061/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Recent advances in microscopy allow scientists to generate vast amounts of biological data from a single biopsy sample. Cyclic fluorescence microscopy, in particular, enables multiple targets to be detected simultaneously. This, in turn, has deepened our understanding of tissue composition, cell-to-cell interactions, and cell signaling. Unfortunately, analysis of these datasets can be time-prohibitive due to the sheer volume of data. In this paper, we present CycloNET, a computational pipeline tailored for analyzing raw fluorescent images obtained through cyclic immunofluorescence. The automated pipeline pre-processes raw image files, quickly corrects for translation errors between imaging cycles, and leverages a pre-trained neural network to segment individual cells and generate single-cell molecular profiles. We applied CycloNET to a dataset of 22 human samples from head and neck squamous cell carcinoma patients and trained a neural network to segment immune cells. CycloNET efficiently processed a large-scale dataset (17 fields of view per cycle and 13 staining cycles per specimen) in 10 minutes, delivering insights at the single-cell resolution and facilitating the identification of rare immune cell clusters. We expect that this rapid pipeline will serve as a powerful tool to understand complex biological systems at the cellular level, with the potential to facilitate breakthroughs in areas such as developmental biology, disease pathology, and personalized medicine.
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Affiliation(s)
- Christian Landeros
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard-MIT Program in Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Juhyun Oh
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Ralph Weissleder
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Hakho Lee
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
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17
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Alexandrov T, Saez‐Rodriguez J, Saka SK. Enablers and challenges of spatial omics, a melting pot of technologies. Mol Syst Biol 2023; 19:e10571. [PMID: 37842805 PMCID: PMC10632737 DOI: 10.15252/msb.202110571] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 10/17/2023] Open
Abstract
Spatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot.
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Affiliation(s)
- Theodore Alexandrov
- Structural and Computational Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- BioInnovation InstituteCopenhagenDenmark
| | - Julio Saez‐Rodriguez
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Faculty of Medicine and Heidelberg University Hospital, Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Sinem K Saka
- Genome Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
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18
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Han X, Li PH, Wang S, Sanchez M, Aggarwal S, Blakely T, Schalek R, Meirovitch Y, Lin Z, Berger D, Wu Y, Aly F, Bay S, Delatour B, LaFaye P, Pfister H, Wei D, Jain V, Ploegh H, Lichtman J. A large-scale volumetric correlated light and electron microscopy study localizes Alzheimer's disease-related molecules in the hippocampus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.24.563674. [PMID: 37961104 PMCID: PMC10634883 DOI: 10.1101/2023.10.24.563674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Connectomics is a nascent neuroscience field to map and analyze neuronal networks. It provides a new way to investigate abnormalities in brain tissue, including in models of Alzheimer's disease (AD). This age-related disease is associated with alterations in amyloid-β (Aβ) and phosphorylated tau (pTau). These alterations correlate with AD's clinical manifestations, but causal links remain unclear. Therefore, studying these molecular alterations within the context of the local neuronal and glial milieu may provide insight into disease mechanisms. Volume electron microscopy (vEM) is an ideal tool for performing connectomics studies at the ultrastructural level, but localizing specific biomolecules within large-volume vEM data has been challenging. Here we report a volumetric correlated light and electron microscopy (vCLEM) approach using fluorescent nanobodies as immuno-probes to localize Alzheimer's disease-related molecules in a large vEM volume. Three molecules (pTau, Aβ, and a marker for activated microglia (CD11b)) were labeled without the need for detergents by three nanobody probes in a sample of the hippocampus of the 3xTg Alzheimer's disease model mouse. Confocal microscopy followed by vEM imaging of the same sample allowed for registration of the location of the molecules within the volume. This dataset revealed several ultrastructural abnormalities regarding the localizations of Aβ and pTau in novel locations. For example, two pTau-positive post-synaptic spine-like protrusions innervated by axon terminals were found projecting from the axon initial segment of a pyramidal cell. Three pyramidal neurons with intracellular Aβ or pTau were 3D reconstructed. Automatic synapse detection, which is necessary for connectomics analysis, revealed the changes in density and volume of synapses at different distances from an Aβ plaque. This vCLEM approach is useful to uncover molecular alterations within large-scale volume electron microscopy data, opening a new connectomics pathway to study Alzheimer's disease and other types of dementia.
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19
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KIM S, KAMARULZAMAN L, TANIGUCHI Y. Recent methodological advances towards single-cell proteomics. PROCEEDINGS OF THE JAPAN ACADEMY. SERIES B, PHYSICAL AND BIOLOGICAL SCIENCES 2023; 99:306-327. [PMID: 37673661 PMCID: PMC10749393 DOI: 10.2183/pjab.99.021] [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: 02/14/2023] [Accepted: 07/20/2023] [Indexed: 09/08/2023]
Abstract
Studying the central dogma at the single-cell level has gained increasing attention to reveal hidden cell lineages and functions that cannot be studied using traditional bulk analyses. Nonetheless, most single-cell studies exploiting genomic and transcriptomic levels fail to address information on proteins that are central to many important biological processes. Single-cell proteomics enables understanding of the functional status of individual cells and is particularly crucial when the specimen is composed of heterogeneous entities of cells. With the growing importance of this field, significant methodological advancements have emerged recently. These include miniaturized and automated sample preparation, multi-omics analyses, and combined analyses of multiple techniques such as mass spectrometry and microscopy. Moreover, artificial intelligence and single-molecule detection technologies have advanced throughput and improved sensitivity limitations, respectively, over conventional methods. In this review, we summarize cutting-edge methodologies for single-cell proteomics and relevant emerging technologies that have been reported in the last 5 years, and provide an outlook on this research field.
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Affiliation(s)
- Sooyeon KIM
- Laboratory for Cell Systems Control, Center for Biosystems Dynamics Research, RIKEN, Suita, Osaka, Japan
- Institute for Integrated Cell-Material Sciences (iCeMS), Kyoto University, Sakyo-ku, Kyoto, Japan
| | - Latiefa KAMARULZAMAN
- Laboratory for Cell Systems Control, Center for Biosystems Dynamics Research, RIKEN, Suita, Osaka, Japan
- Graduate School of Frontier Biosciences, Osaka University, Suita, Osaka, Japan
| | - Yuichi TANIGUCHI
- Laboratory for Cell Systems Control, Center for Biosystems Dynamics Research, RIKEN, Suita, Osaka, Japan
- Institute for Integrated Cell-Material Sciences (iCeMS), Kyoto University, Sakyo-ku, Kyoto, Japan
- Graduate School of Frontier Biosciences, Osaka University, Suita, Osaka, Japan
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20
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Zhang H, Papadaki S, Sun X, Wang X, Drobizhev M, Yao L, Rehbock M, Köster RW, Wu L, Namikawa K, Piatkevich KD. Quantitative assessment of near-infrared fluorescent proteins. Nat Methods 2023; 20:1605-1616. [PMID: 37666982 DOI: 10.1038/s41592-023-01975-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 06/29/2023] [Indexed: 09/06/2023]
Abstract
Recent progress in fluorescent protein development has generated a large diversity of near-infrared fluorescent proteins (NIR FPs), which are rapidly becoming popular probes for a variety of imaging applications. However, the diversity of NIR FPs poses a challenge for end-users in choosing the optimal one for a given application. Here we conducted a systematic and quantitative assessment of intracellular brightness, photostability, oligomeric state, chemical stability and cytotoxicity of 22 NIR FPs in cultured mammalian cells and primary mouse neurons and identified a set of top-performing FPs including emiRFP670, miRFP680, miRFP713 and miRFP720, which can cover a majority of imaging applications. The top-performing proteins were further validated for in vivo imaging of neurons in Caenorhabditis elegans, zebrafish, and mice as well as in mice liver. We also assessed the applicability of the selected NIR FPs for multicolor imaging of fusions, expansion microscopy and two-photon imaging.
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Affiliation(s)
- Hanbin Zhang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | - Stavrini Papadaki
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | - Xiaoting Sun
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | - Xinyue Wang
- Division of Cellular and Molecular Neurobiology, Zoological Institute, Technische Universität Braunschweig, Braunschweig, Germany
| | - Mikhail Drobizhev
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT, USA
| | - Luxia Yao
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | - Michel Rehbock
- Division of Cellular and Molecular Neurobiology, Zoological Institute, Technische Universität Braunschweig, Braunschweig, Germany
| | - Reinhard W Köster
- Division of Cellular and Molecular Neurobiology, Zoological Institute, Technische Universität Braunschweig, Braunschweig, Germany
| | - Lianfeng Wu
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | - Kazuhiko Namikawa
- Division of Cellular and Molecular Neurobiology, Zoological Institute, Technische Universität Braunschweig, Braunschweig, Germany
| | - Kiryl D Piatkevich
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China.
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China.
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21
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Jiang Y, Sha H, Liu S, Qin P, Zhang Y. AutoUnmix: an autoencoder-based spectral unmixing method for multi-color fluorescence microscopy imaging. BIOMEDICAL OPTICS EXPRESS 2023; 14:4814-4827. [PMID: 37791286 PMCID: PMC10545201 DOI: 10.1364/boe.498421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 08/12/2023] [Accepted: 08/14/2023] [Indexed: 10/05/2023]
Abstract
Multiplexed fluorescence microscopy imaging is widely used in biomedical applications. However, simultaneous imaging of multiple fluorophores can result in spectral leaks and overlapping, which greatly degrades image quality and subsequent analysis. Existing popular spectral unmixing methods are mainly based on computational intensive linear models, and the performance is heavily dependent on the reference spectra, which may greatly preclude its further applications. In this paper, we propose a deep learning-based blindly spectral unmixing method, termed AutoUnmix, to imitate the physical spectral mixing process. A transfer learning framework is further devised to allow our AutoUnmix to adapt to a variety of imaging systems without retraining the network. Our proposed method has demonstrated real-time unmixing capabilities, surpassing existing methods by up to 100-fold in terms of unmixing speed. We further validate the reconstruction performance on both synthetic datasets and biological samples. The unmixing results of AutoUnmix achieve the highest SSIM of 0.99 in both three- and four-color imaging, with nearly up to 20% higher than other popular unmixing methods. For experiments where spectral profiles and morphology are akin to simulated data, our method realizes the quantitative performance demonstrated above. Due to the desirable property of data independency and superior blind unmixing performance, we believe AutoUnmix is a powerful tool for studying the interaction process of different organelles labeled by multiple fluorophores.
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Affiliation(s)
- Yuan Jiang
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China
| | - Hao Sha
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China
| | - Shuai Liu
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong Province 518055, China
| | - Peiwu Qin
- Center of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Guangdong Province, 518055, China
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong Province, 518055, China
| | - Yongbing Zhang
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China
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22
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La W, Seo J, Heo E, Chang JB. Expandable ELAST for super-resolution imaging of thick tissue slices using a hydrogel containing charged monomers. Sci Rep 2023; 13:11929. [PMID: 37488163 PMCID: PMC10366192 DOI: 10.1038/s41598-023-38891-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 07/17/2023] [Indexed: 07/26/2023] Open
Abstract
Hydrogels have been utilized extensively as a material for retaining position information in tissue imaging procedures, such as tissue clearing and super-resolution imaging. Immunostaining thick biological tissues, however, poses a bottleneck that restricts sample size. The recently developed technique known as entangled link-augmented stretchable tissue-hydrogel (ELAST) accelerates the immunostaining process by embedding specimens in long-chain polymers and stretching them. A more advanced version of ELAST, magnifiable entangled link-augmented stretchable tissue-hydrogel (mELAST), achieves rapid immunostaining and tissue expansion by embedding specimens in long-chain neutral polymers and subsequently hydrolyzing them. Building on these techniques, we introduce a variant of mELAST called ExELAST. This approach uses charged monomers to stretch and expand tissue slices. Using ExELAST, we first tested two hydrogel compositions that could permit uniform expansion of biological specimens. Then, we apply the tailored hydrogel to the 500-μm-thick mouse brain slices and demonstrated that they can be stained within two days and imaged with a resolution below the diffraction limit of light.
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Affiliation(s)
- Woonggi La
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, South Korea
| | - Junyoung Seo
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, South Korea
| | - Eunseok Heo
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, South Korea
| | - Jae-Byum Chang
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, South Korea.
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, 34141, South Korea.
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23
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Han X, Lu X, Li PH, Wang S, Schalek R, Meirovitch Y, Lin Z, Adhinarta J, Berger D, Wu Y, Fang T, Meral ES, Asraf S, Ploegh H, Pfister H, Wei D, Jain V, Trimmer JS, Lichtman JW. Multiplexed volumetric CLEM enabled by antibody derivatives provides new insights into the cytology of the mouse cerebellar cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.20.540091. [PMID: 37292964 PMCID: PMC10245788 DOI: 10.1101/2023.05.20.540091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Mapping neuronal networks that underlie behavior has become a central focus in neuroscience. While serial section electron microscopy (ssEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide the molecular information that helps identify cell types or their functional properties. Volumetric correlated light and electron microscopy (vCLEM) combines ssEM and volumetric fluorescence microscopy to incorporate molecular labeling into ssEM datasets. We developed an approach that uses small fluorescent single-chain variable fragment (scFv) immuno-probes to perform multiplexed detergent-free immuno-labeling and ssEM on the same samples. We generated eight such fluorescent scFvs that targeted useful markers for brain studies (green fluorescent protein, glial fibrillary acidic protein, calbindin, parvalbumin, voltage-gated potassium channel subfamily A member 2, vesicular glutamate transporter 1, postsynaptic density protein 95, and neuropeptide Y). To test the vCLEM approach, six different fluorescent probes were imaged in a sample of the cortex of a cerebellar lobule (Crus 1), using confocal microscopy with spectral unmixing, followed by ssEM imaging of the same sample. The results show excellent ultrastructure with superimposition of the multiple fluorescence channels. Using this approach we could document a poorly described cell type in the cerebellum, two types of mossy fiber terminals, and the subcellular localization of one type of ion channel. Because scFvs can be derived from existing monoclonal antibodies, hundreds of such probes can be generated to enable molecular overlays for connectomic studies.
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24
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Han X, Lu X, Li PH, Wang S, Schalek R, Meirovitch Y, Lin Z, Adhinarta J, Berger D, Wu Y, Fang T, Meral ES, Asraf S, Ploegh H, Pfister H, Wei D, Jain V, Trimmer JS, Lichtman JW. Multiplexed volumetric CLEM enabled by antibody derivatives provides new insights into the cytology of the mouse cerebellar cortex. RESEARCH SQUARE 2023:rs.3.rs-3121892. [PMID: 37461609 PMCID: PMC10350204 DOI: 10.21203/rs.3.rs-3121892/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Mapping neuronal networks that underlie behavior has become a central focus in neuroscience. While serial section electron microscopy (ssEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide the molecular information that helps identify cell types or their functional properties. Volumetric correlated light and electron microscopy (vCLEM) combines ssEM and volumetric fluorescence microscopy to incorporate molecular labeling into ssEM datasets. We developed an approach that uses small fluorescent single-chain variable fragment (scFv) immuno-probes to perform multiplexed detergent-free immuno-labeling and ssEM on the same samples. We generated eight such fluorescent scFvs that targeted useful markers for brain studies (green fluorescent protein, glial fibrillary acidic protein, calbindin, parvalbumin, voltage-gated potassium channel subfamily A member 2, vesicular glutamate transporter 1, postsynaptic density protein 95, and neuropeptide Y). To test the vCLEM approach, six different fluorescent probes were imaged in a sample of the cortex of a cerebellar lobule (Crus 1), using confocal microscopy with spectral unmixing, followed by ssEM imaging of the same sample. The results show excellent ultrastructure with superimposition of the multiple fluorescence channels. Using this approach we could document a poorly described cell type in the cerebellum, two types of mossy fiber terminals, and the subcellular localization of one type of ion channel. Because scFvs can be derived from existing monoclonal antibodies, hundreds of such probes can be generated to enable molecular overlays for connectomic studies.
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Affiliation(s)
- Xiaomeng Han
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
| | - Xiaotang Lu
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
| | | | - Shuohong Wang
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
| | - Richard Schalek
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
| | - Yaron Meirovitch
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
| | - Zudi Lin
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - Jason Adhinarta
- Computer Science Department, Boston College, Chestnut Hill, MA
| | - Daniel Berger
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
| | - Yuelong Wu
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
| | - Tao Fang
- Program of Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
| | | | - Shadnan Asraf
- School of Public Health, University of Massachusetts Amherst, Amherst, MA
| | - Hidde Ploegh
- Program of Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
| | - Hanspeter Pfister
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - Donglai Wei
- Computer Science Department, Boston College, Chestnut Hill, MA
| | | | - James S. Trimmer
- Department of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, CA
| | - Jeff W. Lichtman
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
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25
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Ji F, Hur M, Hur S, Wang S, Sarkar P, Shao S, Aispuro D, Cong X, Hu Y, Li Z, Xue M. Multiplex Protein Imaging through PACIFIC: Photoactive Immunofluorescence with Iterative Cleavage. ACS BIO & MED CHEM AU 2023; 3:283-294. [PMID: 37363079 PMCID: PMC10288499 DOI: 10.1021/acsbiomedchemau.3c00018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 04/14/2023] [Accepted: 04/17/2023] [Indexed: 06/28/2023]
Abstract
Multiplex protein imaging technologies enable deep phenotyping and provide rich spatial information about biological samples. Existing methods have shown great success but also harbored trade-offs between various pros and cons, underscoring the persisting necessity to expand the imaging toolkits. Here we present PACIFIC: photoactive immunofluorescence with iterative cleavage, a new modality of multiplex protein imaging methods. PACIFIC achieves iterative multiplexing by implementing photocleavable fluorophores for antibody labeling with one-step spin-column purification. PACIFIC requires no specialized instrument, no DNA encoding, or chemical treatments. We demonstrate that PACIFIC can resolve cellular heterogeneity in both formalin-fixed paraffin-embedded (FFPE) samples and fixed cells. To further highlight how PACIFIC assists discovery, we integrate PACIFIC with live-cell tracking and identify phosphor-p70S6K as a critical driver that governs U87 cell mobility. Considering the cost, flexibility, and compatibility, we foresee that PACIFIC can confer deep phenotyping capabilities to anyone with access to traditional immunofluorescence platforms.
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Affiliation(s)
- Fei Ji
- Department
of Chemistry, University of California,
Riverside, Riverside, California 92521, United States
| | - Moises Hur
- Martin
Luther King Jr High School, Riverside, California 92508, United States
| | - Sungwon Hur
- Martin
Luther King Jr High School, Riverside, California 92508, United States
| | - Siwen Wang
- Department
of Chemistry, University of California,
Riverside, Riverside, California 92521, United States
- Environmental
Toxicology Graduate Program, University
of California, Riverside, Riverside, California 92521, United States
| | - Priyanka Sarkar
- Department
of Chemistry, University of California,
Riverside, Riverside, California 92521, United States
| | - Shiqun Shao
- Department
of Chemistry, University of California,
Riverside, Riverside, California 92521, United States
- College
of Chemical and Biological Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, P.R. China
| | - Desiree Aispuro
- Department
of Chemistry, University of California,
Riverside, Riverside, California 92521, United States
- Environmental
Toxicology Graduate Program, University
of California, Riverside, Riverside, California 92521, United States
| | - Xu Cong
- Department
of Chemistry, University of California,
Riverside, Riverside, California 92521, United States
| | - Yanhao Hu
- Diamond
Bar High School, Diamond
Bar, California 91765, United States
| | - Zhonghan Li
- Department
of Chemistry, University of California,
Riverside, Riverside, California 92521, United States
| | - Min Xue
- Department
of Chemistry, University of California,
Riverside, Riverside, California 92521, United States
- Environmental
Toxicology Graduate Program, University
of California, Riverside, Riverside, California 92521, United States
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26
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Zheng R, Wu R, Liu Y, Sun Z, Bagheri Y, Xue Z, Mi L, Tian Q, Pho R, Siddiqui S, Ren K, You M. Multiplexed Sequential Imaging in Living Cells with Orthogonal Fluorogenic RNA Aptamer/Dye Pairs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.20.537750. [PMID: 37131625 PMCID: PMC10153257 DOI: 10.1101/2023.04.20.537750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Single-cell detection of multiple target analytes is an important goal in cell biology. However, due to the spectral overlap of common fluorophores, multiplexed fluorescence imaging beyond two-to-three targets inside living cells remains a technical challenge. Herein, we introduce a multiplexed imaging strategy that enables live-cell target detection via sequential rounds of imaging-and-stripping process, which is named as "sequential Fluorogenic RNA Imaging-Enabled Sensor" (seqFRIES). In seqFRIES, multiple orthogonal fluorogenic RNA aptamers are genetically encoded inside cells, and then the corresponding cell membrane permeable dye molecules are added, imaged, and rapidly removed in consecutive detection cycles. As a proof-of-concept, we have identified in this study five in vitro orthogonal fluorogenic RNA aptamer/dye pairs (>10-fold higher fluorescence signals), four of which can be used for highly orthogonal and multiplexed imaging in living bacterial and mammalian cells. After further optimizing the cellular fluorescence activation and deactivation kinetics of these RNA/dye pairs, the whole four-color semi-quantitative seqFRIES process can now be completed in ~20 min. Meanwhile, seqFRIES-mediated simultaneous detection of two critical signaling molecules, guanosine tetraphosphate and cyclic diguanylate, was also achieved within individual living cells. We expect our validation of this new seqFRIES concept here will facilitate the further development and potential broad usage of these orthogonal fluorogenic RNA/dye pairs for highly multiplexed and dynamic cellular imaging and cell biology studies.
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Affiliation(s)
- Ru Zheng
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
| | - Rigumula Wu
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
| | - Yuanchang Liu
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
| | - Zhining Sun
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
| | - Yousef Bagheri
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
| | - Zhaolin Xue
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
| | - Lan Mi
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
| | - Qian Tian
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
| | - Raymond Pho
- Department of Chemical Engineering, University of Massachusetts, Amherst, MA 01003, USA
| | - Sidrat Siddiqui
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
| | - Kewei Ren
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
- School of Chemistry and Chemical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Mingxu You
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
- Molecular and Cellular Biology Program, University of Massachusetts, Amherst, MA 01003, USA
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27
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Harris A, Andl T. Precancerous Lesions of the Head and Neck Region and Their Stromal Aberrations: Piecemeal Data. Cancers (Basel) 2023; 15:cancers15082192. [PMID: 37190121 DOI: 10.3390/cancers15082192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/04/2023] [Accepted: 04/05/2023] [Indexed: 05/17/2023] Open
Abstract
Head and neck squamous cell carcinomas (HNSCCs) develop through a series of precancerous stages from a pool of potentially malignant disorders (PMDs). Although we understand the genetic changes that lead to HNSCC, our understanding of the role of the stroma in the progression from precancer to cancer is limited. The stroma is the primary battleground between the forces that prevent and promote cancer growth. Targeting the stroma has yielded promising cancer therapies. However, the stroma at the precancerous stage of HNSCCs is poorly defined, and we may miss opportunities for chemopreventive interventions. PMDs already exhibit many features of the HNSCC stroma, such as inflammation, neovascularization, and immune suppression. Still, they do not induce cancer-associated fibroblasts or destroy the basal lamina, the stroma's initial structure. Our review aims to summarize the current understanding of the transition from precancer to cancer stroma and how this knowledge can reveal opportunities and limitations for diagnostic, prognostic, and therapeutic decisions to benefit patients. We will discuss what may be needed to fulfill the promise of the precancerous stroma as a target to prevent progression to cancer.
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Affiliation(s)
- Ashlee Harris
- Burnett School of Biomedical Sciences, University of Central Florida, 12722 Research Pkwy, Orlando, FL 32826, USA
| | - Thomas Andl
- Burnett School of Biomedical Sciences, University of Central Florida, 12722 Research Pkwy, Orlando, FL 32826, USA
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28
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Locke RC, Zlotnick HM, Stoeckl BD, Fryhofer GW, Galarraga JH, Dhand AP, Zgonis MH, Carey JL, Burdick JA, Mauck RL. Linguistic Analysis Identifies Emergent Biomaterial Fabrication Trends for Orthopaedic Applications. Adv Healthc Mater 2023; 12:e2202591. [PMID: 36657736 PMCID: PMC10121863 DOI: 10.1002/adhm.202202591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 12/20/2022] [Indexed: 01/21/2023]
Abstract
The expanse of publications in tissue engineering (TE) and orthopedic TE (OTE) over the past 20 years presents an opportunity to probe emergent trends in the field to better guide future technologies that can make an impact on musculoskeletal therapies. Leveraging this trove of knowledge, a hierarchical systematic search method and trend analysis using connected network mapping of key terms is developed. Within discrete time intervals, an accelerated publication rate for anatomic orthopedic tissue engineering (AOTE) of osteochondral defects, tendons, menisci, and entheses is identified. Within these growing fields, the top-listed key terms are extracted and stratified into evident categories, such as biomaterials, delivery method, or 3D printing and biofabrication. It is then identified which categories decreased, remained constant, increased, or emerged over time, identifying the specific emergent categories currently driving innovation in orthopedic repair technologies. Together, these data demonstrate a significant convergence of material types and descriptors used across tissue types. From this convergence, design criteria to support future research of anatomic constructs that mimic both the form and function of native tissues are formulated. In summary, this review identifies large-scale trends and predicts new directions in orthopedics that will define future materials and technologies.
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Affiliation(s)
- Ryan C. Locke
- Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia, PA, USA
- Department of Veterans Affairs, CMCVAMC, Philadelphia, PA, USA
| | - Hannah M. Zlotnick
- Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Veterans Affairs, CMCVAMC, Philadelphia, PA, USA
| | - Brendan D. Stoeckl
- Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Veterans Affairs, CMCVAMC, Philadelphia, PA, USA
| | - George W. Fryhofer
- Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Abhishek P. Dhand
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Miltiadis H. Zgonis
- Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia, PA, USA
| | - James L. Carey
- Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia, PA, USA
| | - Jason A. Burdick
- Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO, USA
- BioFrontiers Institute, University of Colorado, Boulder, CO, USA
| | - Robert L. Mauck
- Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Veterans Affairs, CMCVAMC, Philadelphia, PA, USA
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29
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Shahidehpour RK, Nelson AS, Sanders LG, Embry CR, Nelson PT, Bachstetter AD. The localization of molecularly distinct microglia populations to Alzheimer's disease pathologies using QUIVER. Acta Neuropathol Commun 2023; 11:45. [PMID: 36934255 PMCID: PMC10024857 DOI: 10.1186/s40478-023-01541-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 03/03/2023] [Indexed: 03/20/2023] Open
Abstract
New histological techniques are needed to examine protein distribution in human tissues, which can reveal cell shape and disease pathology connections. Spatial proteomics has changed the study of tumor microenvironments by identifying spatial relationships of immunomodulatory cells and proteins and contributing to the discovery of new cancer immunotherapy biomarkers. However, the fast-expanding toolkit of spatial proteomic approaches has yet to be systematically applied to investigate pathological alterations in the aging human brain in health and disease states. Moreover, post-mortem human brain tissue presents distinct technical problems due to fixation procedures and autofluorescence, which limit fluorescence methodologies. This study sought to develop a multiplex immunohistochemistry approach (visualizing the immunostain with brightfield microscopy). Quantitative multiplex Immunohistochemistry with Visual colorimetric staining to Enhance Regional protein localization (QUIVER) was developed to address these technical challenges. Using QUIVER, a ten-channel pseudo-fluorescent image was generated using chromogen removal and digital microscopy to identify unique molecular microglia phenotypes. Next, the study asked if the tissue environment, specifically the amyloid plaques and neurofibrillary tangles characteristic of Alzheimer's disease, has any bearing on microglia's cellular and molecular phenotypes. QUIVER allowed the visualization of five molecular microglia/macrophage phenotypes using digital pathology tools. The recognizable reactive and homeostatic microglia/macrophage phenotypes demonstrated spatial polarization towards and away from amyloid plaques, respectively. Yet, microglia morphology appearance did not always correspond to molecular phenotype. This research not only sheds light on the biology of microglia but also offers QUIVER, a new tool for examining pathological alterations in the brains of the elderly.
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Affiliation(s)
- Ryan K Shahidehpour
- Spinal Cord and Brain Injury Research Center, University of Kentucky, 741 S. Limestone St., Lexington, KY, 40536, USA
- Department of Neuroscience, University of Kentucky, Lexington, KY, 40536, USA
| | - Abraham S Nelson
- Spinal Cord and Brain Injury Research Center, University of Kentucky, 741 S. Limestone St., Lexington, KY, 40536, USA
| | - Lydia G Sanders
- Spinal Cord and Brain Injury Research Center, University of Kentucky, 741 S. Limestone St., Lexington, KY, 40536, USA
| | - Chloe R Embry
- Spinal Cord and Brain Injury Research Center, University of Kentucky, 741 S. Limestone St., Lexington, KY, 40536, USA
| | - Peter T Nelson
- Department of Neuroscience, University of Kentucky, Lexington, KY, 40536, USA
- Sanders-Brown Center On Aging, University of Kentucky, Lexington, KY, 40536, USA
- Division of Neuropathology, Department of Pathology and Laboratory Medicine, University of Kentucky, Lexington, KY, 40536, USA
| | - Adam D Bachstetter
- Spinal Cord and Brain Injury Research Center, University of Kentucky, 741 S. Limestone St., Lexington, KY, 40536, USA.
- Department of Neuroscience, University of Kentucky, Lexington, KY, 40536, USA.
- Sanders-Brown Center On Aging, University of Kentucky, Lexington, KY, 40536, USA.
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30
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van Ineveld RL, Collot R, Román MB, Pagliaro A, Bessler N, Ariese HCR, Kleinnijenhuis M, Kool M, Alieva M, Chuva de Sousa Lopes SM, Wehrens EJ, Rios AC. Multispectral confocal 3D imaging of intact healthy and tumor tissue using mLSR-3D. Nat Protoc 2022; 17:3028-3055. [PMID: 36180532 DOI: 10.1038/s41596-022-00739-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 06/16/2022] [Indexed: 11/09/2022]
Abstract
Revealing the 3D composition of intact tissue specimens is essential for understanding cell and organ biology in health and disease. State-of-the-art 3D microscopy techniques aim to capture tissue volumes on an ever-increasing scale, while also retaining sufficient resolution for single-cell analysis. Furthermore, spatial profiling through multi-marker imaging is fast developing, providing more context and better distinction between cell types. Following these lines of technological advance, we here present a protocol based on FUnGI (fructose, urea and glycerol clearing solution for imaging) optical clearing of tissue before multispectral large-scale single-cell resolution 3D (mLSR-3D) imaging, which implements 'on-the-fly' linear unmixing of up to eight fluorophores during a single acquisition. Our protocol removes the need for repetitive illumination, thereby allowing larger volumes to be scanned with better image quality in less time, also reducing photo-bleaching and file size. To aid in the design of multiplex antibody panels, we provide a fast and manageable intensity equalization assay with automated analysis to design a combination of markers with balanced intensities suitable for mLSR-3D. We demonstrate effective mLSR-3D imaging of various tissues, including patient-derived organoids and xenografted tumors, and, furthermore, describe an optimized workflow for mLSR-3D imaging of formalin-fixed paraffin-embedded samples. Finally, we provide essential steps for 3D image data processing, including shading correction that does not require pre-acquired shading references and 3D inhomogeneity correction to correct fluorescence artefacts often afflicting 3D datasets. Together, this provides a one-week protocol for eight-fluorescent-marker 3D visualization and exploration of intact tissue of various origins at single-cell resolution.
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Affiliation(s)
- Ravian L van Ineveld
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Raphaël Collot
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Mario Barrera Román
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Anna Pagliaro
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Nils Bessler
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Hendrikus C R Ariese
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Michiel Kleinnijenhuis
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Marcel Kool
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
- Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany
- Division of Pediatric Neurooncology, German Cancer Research Center DKFZ and German Cancer Consortium DKTK, Heidelberg, Germany
| | - Maria Alieva
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | | | - Ellen J Wehrens
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Anne C Rios
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands.
- Oncode Institute, Utrecht, the Netherlands.
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31
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Brennan CK, Yao Z, Ionkina AA, Rathbun CM, Sathishkumar B, Prescher JA. Multiplexed bioluminescence imaging with a substrate unmixing platform. Cell Chem Biol 2022; 29:1649-1660.e4. [PMID: 36283402 PMCID: PMC9675729 DOI: 10.1016/j.chembiol.2022.10.004] [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: 03/08/2022] [Revised: 08/31/2022] [Accepted: 09/30/2022] [Indexed: 01/31/2023]
Abstract
Bioluminescent tools can illuminate cellular features in whole organisms. Multi-component tracking remains challenging, though, owing to a lack of well-resolved probes and long imaging times. To address the need for more rapid, quantitative, and multiplexed bioluminescent readouts, we developed an analysis pipeline featuring sequential substrate administration and serial image acquisition. Light output from each luciferin is layered on top of the previous image, with minimal delay between substrate delivery. A MATLAB algorithm was written to analyze bioluminescent images generated from the rapid imaging protocol and deconvolute (i.e., unmix) signals from luciferase-luciferin pairs. Mixtures comprising three to five luciferase reporters were readily distinguished in under 50 min; this same experiment would require days using conventional workflows. We further showed that the algorithm can be used to accurately quantify luciferase levels in heterogeneous mixtures. Based on its speed and versatility, the multiplexed imaging platform will expand the scope of bioluminescence technology.
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Affiliation(s)
- Caroline K Brennan
- Department of Chemistry, University of California, Irvine, Irvine, CA 92697, USA
| | - Zi Yao
- Department of Chemistry, University of California, Irvine, Irvine, CA 92697, USA
| | - Anastasia A Ionkina
- Department of Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA 92697, USA
| | - Colin M Rathbun
- Department of Chemistry, University of California, Irvine, Irvine, CA 92697, USA
| | | | - Jennifer A Prescher
- Department of Chemistry, University of California, Irvine, Irvine, CA 92697, USA; Department of Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA 92697, USA; Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA 92697, USA.
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Chen K, Li W, Xu K. Super-multiplexing excitation spectral microscopy with multiple fluorescence bands. BIOMEDICAL OPTICS EXPRESS 2022; 13:6048-6060. [PMID: 36733753 PMCID: PMC9872899 DOI: 10.1364/boe.473241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 10/16/2022] [Accepted: 10/17/2022] [Indexed: 06/18/2023]
Abstract
Fluorescence microscopy, with high molecular specificity and selectivity, is a valuable tool for studying complex biological systems and processes. However, the ability to distinguish a large number of distinct subcellular structures in a single sample is impeded by the broad spectra of molecular fluorescence. We have recently shown that excitation spectral microscopy provides a powerful means to unmix up to six fluorophores in a single fluorescence band. Here, by working with multiple fluorescence bands, we extend this approach to the simultaneous imaging of up to ten targets, with the potential for further expansions. By covering the excitation/emission bandwidth across the full visible range, an ultra-broad 24-wavelength excitation scheme is established through frame-synchronized scanning of the excitation wavelength from a white lamp via an acousto-optic tunable filter (AOTF), so that full-frame excitation-spectral images are obtained every 24 camera frames, offering superior spectral information and multiplexing capability. With numerical simulations, we validate the concurrent imaging of 10 fluorophores spanning the visible range to achieve exceptionally low (∼0.5%) crosstalks. For cell imaging experiments, we demonstrate unambiguous identification of up to eight different intracellular structures labeled by common fluorophores of substantial spectral overlap with minimal color crosstalks. We thus showcase an easy-to-implement, cost-effective microscopy system for visualizing complex cellular components with more colors and lower crosstalks.
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Affiliation(s)
- Kun Chen
- Department of Chemistry & California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, CA 94720, USA
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Wan Li
- Department of Chemistry & California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Ke Xu
- Department of Chemistry & California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, CA 94720, USA
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Enninful A, Baysoy A, Fan R. Unmixing for ultra-high-plex fluorescence imaging. Nat Commun 2022; 13:3473. [PMID: 35710800 PMCID: PMC9203536 DOI: 10.1038/s41467-022-31110-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 06/06/2022] [Indexed: 12/02/2022] Open
Affiliation(s)
- Archibald Enninful
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Alev Baysoy
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Rong Fan
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA. .,Department of Pathology, Yale School of Medicine, New Haven, CT, 06520, USA. .,Yale Cancer Center and Yale Stem Cell Center, Yale School of Medicine, New Haven, CT, 06520, USA. .,Human and Translational Immunology Program, Yale School of Medicine, New Haven, CT, 06520, USA.
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