1
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Bishop KW, Erion Barner LA, Baraznenok E, Lan L, Poudel C, Brenes D, Serafin RB, True LD, Vaughan JC, Glaser AK, Liu JTC. Axially swept open-top light-sheet microscopy for densely labeled clinical specimens. OPTICS LETTERS 2024; 49:3794-3797. [PMID: 38950270 DOI: 10.1364/ol.521591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 05/17/2024] [Indexed: 07/03/2024]
Abstract
Open-top light-sheet (OTLS) microscopy offers rapid 3D imaging of large optically cleared specimens. This enables nondestructive 3D pathology, which provides key advantages over conventional slide-based histology including comprehensive sampling without tissue sectioning/destruction and visualization of diagnostically important 3D structures. With 3D pathology, clinical specimens are often labeled with small-molecule stains that broadly target nucleic acids and proteins, mimicking conventional hematoxylin and eosin (H&E) dyes. Tight optical sectioning helps to minimize out-of-focus fluorescence for high-contrast imaging in these densely labeled tissues but has been challenging to achieve in OTLS systems due to trade-offs between optical sectioning and field of view. Here we present an OTLS microscope with voice-coil-based axial sweeping to circumvent this trade-off, achieving 2 µm axial resolution over a 750 × 375 µm field of view. We implement our design in a non-orthogonal dual-objective (NODO) architecture, which enables a 10-mm working distance with minimal sensitivity to refractive index mismatches, for high-contrast 3D imaging of clinical specimens.
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2
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Zheng J, Wu YC, Phillips EH, Cai X, Wang X, Seung-Young Lee S. Increased Multiplexity in Optical Tissue Clearing-Based Three-Dimensional Immunofluorescence Microscopy of the Tumor Microenvironment by Light-Emitting Diode Photobleaching. J Transl Med 2024; 104:102072. [PMID: 38679160 PMCID: PMC11240282 DOI: 10.1016/j.labinv.2024.102072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/29/2024] [Accepted: 04/19/2024] [Indexed: 05/01/2024] Open
Abstract
Optical tissue clearing and three-dimensional (3D) immunofluorescence (IF) microscopy is transforming imaging of the complex tumor microenvironment (TME). However, current 3D IF microscopy has restricted multiplexity; only 3 or 4 cellular and noncellular TME components can be localized in cleared tumor tissue. Here we report a light-emitting diode (LED) photobleaching method and its application for 3D multiplexed optical mapping of the TME. We built a high-power LED light irradiation device and temperature-controlled chamber for completely bleaching fluorescent signals throughout optically cleared tumor tissues without compromise of tissue and protein antigen integrity. With newly developed tissue mounting and selected region-tracking methods, we established a cyclic workflow involving IF staining, tissue clearing, 3D confocal microscopy, and LED photobleaching. By registering microscope channel images generated through 3 work cycles, we produced 8-plex image data from individual 400 μm-thick tumor macrosections that visualize various vascular, immune, and cancer cells in the same TME at tissue-wide and cellular levels in 3D. Our method was also validated for quantitative 3D spatial analysis of cellular remodeling in the TME after immunotherapy. These results demonstrate that our LED photobleaching system and its workflow offer a novel approach to increase the multiplexing power of 3D IF microscopy for studying tumor heterogeneity and response to therapy.
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Affiliation(s)
- Jingtian Zheng
- Department of Pharmaceutical Sciences, University of Illinois, Chicago, Chicago, Illinois
| | - Yi-Chien Wu
- Department of Pharmaceutical Sciences, University of Illinois, Chicago, Chicago, Illinois
| | - Evan H Phillips
- Department of Pharmaceutical Sciences, University of Illinois, Chicago, Chicago, Illinois
| | - Xiaoying Cai
- Department of Pharmaceutical Sciences, University of Illinois, Chicago, Chicago, Illinois
| | - Xu Wang
- Department of Pharmaceutical Sciences, University of Illinois, Chicago, Chicago, Illinois
| | - Steve Seung-Young Lee
- Department of Pharmaceutical Sciences, University of Illinois, Chicago, Chicago, Illinois; University of Illinois Cancer Center, University of Illinois Chicago, Chicago, Illinois.
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3
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Evergren E, Mills IG, Kennedy G. Adaptations of membrane trafficking in cancer and tumorigenesis. J Cell Sci 2024; 137:jcs260943. [PMID: 38770683 PMCID: PMC11166456 DOI: 10.1242/jcs.260943] [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] [Indexed: 05/22/2024] Open
Abstract
Membrane trafficking, a fundamental cellular process encompassing the transport of molecules to specific organelles, endocytosis at the plasma membrane and protein secretion, is crucial for cellular homeostasis and signalling. Cancer cells adapt membrane trafficking to enhance their survival and metabolism, and understanding these adaptations is vital for improving patient responses to therapy and identifying therapeutic targets. In this Review, we provide a concise overview of major membrane trafficking pathways and detail adaptations in these pathways, including COPII-dependent endoplasmic reticulum (ER)-to-Golgi vesicle trafficking, COPI-dependent retrograde Golgi-to-ER trafficking and endocytosis, that have been found in cancer. We explore how these adaptations confer growth advantages or resistance to cell death and conclude by discussing the potential for utilising this knowledge in developing new treatment strategies and overcoming drug resistance for cancer patients.
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Affiliation(s)
- Emma Evergren
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK
| | - Ian G. Mills
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK
| | - Grace Kennedy
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK
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4
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Bishop KW, Erion Barner LA, Han Q, Baraznenok E, Lan L, Poudel C, Gao G, Serafin RB, Chow SSL, Glaser AK, Janowczyk A, Brenes D, Huang H, Miyasato D, True LD, Kang S, Vaughan JC, Liu JTC. An end-to-end workflow for nondestructive 3D pathology. Nat Protoc 2024; 19:1122-1148. [PMID: 38263522 DOI: 10.1038/s41596-023-00934-4] [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: 07/27/2023] [Accepted: 10/23/2023] [Indexed: 01/25/2024]
Abstract
Recent advances in 3D pathology offer the ability to image orders of magnitude more tissue than conventional pathology methods while also providing a volumetric context that is not achievable with 2D tissue sections, and all without requiring destructive tissue sectioning. Generating high-quality 3D pathology datasets on a consistent basis, however, is not trivial and requires careful attention to a series of details during tissue preparation, imaging and initial data processing, as well as iterative optimization of the entire process. Here, we provide an end-to-end procedure covering all aspects of a 3D pathology workflow (using light-sheet microscopy as an illustrative imaging platform) with sufficient detail to perform well-controlled preclinical and clinical studies. Although 3D pathology is compatible with diverse staining protocols and computationally generated color palettes for visual analysis, this protocol focuses on the use of a fluorescent analog of hematoxylin and eosin, which remains the most common stain used for gold-standard pathological reports. We present our guidelines for a broad range of end users (e.g., biologists, clinical researchers and engineers) in a simple format. The end-to-end workflow requires 3-6 d to complete, bearing in mind that data analysis may take longer.
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Affiliation(s)
- Kevin W Bishop
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | | | - Qinghua Han
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Elena Baraznenok
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Lydia Lan
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
- Department of Biology, University of Washington, Seattle, WA, USA
| | - Chetan Poudel
- Department of Chemistry, University of Washington, Seattle, WA, USA
| | - Gan Gao
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Robert B Serafin
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Sarah S L Chow
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Adam K Glaser
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | - Andrew Janowczyk
- Department of Biomedical Engineering, Emory University, Atlanta, GA, USA
- Department of Oncology, Division of Precision Oncology, University Hospital of Geneva, Geneva, Switzerland
- Department of Diagnostics, Division of Clinical Pathology, University Hospital of Geneva, Geneva, Switzerland
| | - David Brenes
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Hongyi Huang
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Dominie Miyasato
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Lawrence D True
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Soyoung Kang
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Joshua C Vaughan
- Department of Chemistry, University of Washington, Seattle, WA, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Jonathan T C Liu
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA.
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA.
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5
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Abraham TM, Levenson R. Current Landscape of Advanced Imaging Tools for Pathology Diagnostics. Mod Pathol 2024; 37:100443. [PMID: 38311312 DOI: 10.1016/j.modpat.2024.100443] [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] [Received: 07/25/2023] [Revised: 12/13/2023] [Accepted: 01/26/2024] [Indexed: 02/10/2024]
Abstract
Histopathology relies on century-old workflows of formalin fixation, paraffin embedding, sectioning, and staining tissue specimens on glass slides. Despite being robust, this conventional process is slow, labor-intensive, and limited to providing two-dimensional views. Emerging technologies promise to enhance and accelerate histopathology. Slide-free microscopy allows rapid imaging of fresh, unsectioned specimens, overcoming slide preparation delays. Methods such as fluorescence confocal microscopy, multiphoton microscopy, along with more recent innovations including microscopy with UV surface excitation and fluorescence-imitating brightfield imaging can generate images resembling conventional histology directly from the surface of tissue specimens. Slide-free microscopy enable applications such as rapid intraoperative margin assessment and, with appropriate technology, three-dimensional histopathology. Multiomics profiling techniques, including imaging mass spectrometry and Raman spectroscopy, provide highly multiplexed molecular maps of tissues, although clinical translation remains challenging. Artificial intelligence is aiding the adoption of new imaging modalities via virtual staining, which converts methods such as slide-free microscopy into synthetic brightfield-like or even molecularly informed images. Although not yet commonplace, these emerging technologies collectively demonstrate the potential to modernize histopathology. Artificial intelligence-assisted workflows will ease the transition to new imaging modalities. With further validation, these advances may transform the century-old conventional histopathology pipeline to better serve 21st-century medicine. This review provides an overview of these enabling technology platforms and discusses their potential impact.
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Affiliation(s)
- Tanishq Mathew Abraham
- Department of Biomedical Engineering, University of California, Davis, Davis, California
| | - Richard Levenson
- Department of Pathology and Laboratory Medicine, UC Davis Health, Sacramento, California.
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6
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Liu JTC, Chow SSL, Colling R, Downes MR, Farré X, Humphrey P, Janowczyk A, Mirtti T, Verrill C, Zlobec I, True LD. Engineering the future of 3D pathology. J Pathol Clin Res 2024; 10:e347. [PMID: 37919231 PMCID: PMC10807588 DOI: 10.1002/cjp2.347] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 10/06/2023] [Accepted: 10/15/2023] [Indexed: 11/04/2023]
Abstract
In recent years, technological advances in tissue preparation, high-throughput volumetric microscopy, and computational infrastructure have enabled rapid developments in nondestructive 3D pathology, in which high-resolution histologic datasets are obtained from thick tissue specimens, such as whole biopsies, without the need for physical sectioning onto glass slides. While 3D pathology generates massive datasets that are attractive for automated computational analysis, there is also a desire to use 3D pathology to improve the visual assessment of tissue histology. In this perspective, we discuss and provide examples of potential advantages of 3D pathology for the visual assessment of clinical specimens and the challenges of dealing with large 3D datasets (of individual or multiple specimens) that pathologists have not been trained to interpret. We discuss the need for artificial intelligence triaging algorithms and explainable analysis methods to assist pathologists or other domain experts in the interpretation of these novel, often complex, large datasets.
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Affiliation(s)
- Jonathan TC Liu
- Department of Mechanical EngineeringUniversity of WashingtonSeattleWAUSA
- Department of Laboratory Medicine & PathologyUniversity of Washington School of MedicineSeattleUSA
- Department of BioengineeringUniversity of WashingtonSeattleUSA
| | - Sarah SL Chow
- Department of Mechanical EngineeringUniversity of WashingtonSeattleWAUSA
| | | | | | | | - Peter Humphrey
- Department of UrologyYale School of MedicineNew HavenCTUSA
| | - Andrew Janowczyk
- Wallace H Coulter Department of Biomedical EngineeringEmory University and Georgia Institute of TechnologyAtlantaGAUSA
- Geneva University HospitalsGenevaSwitzerland
| | - Tuomas Mirtti
- Helsinki University Hospital and University of HelsinkiHelsinkiFinland
- Emory University School of MedicineAtlantaGAUSA
| | - Clare Verrill
- John Radcliffe HospitalUniversity of OxfordOxfordUK
- NIHR Oxford Biomedical Research CentreOxford University Hospitals NHS Foundation TrustOxfordUK
| | - Inti Zlobec
- Institute for Tissue Medicine and PathologyUniversity of BernBernSwitzerland
| | - Lawrence D True
- Department of Laboratory Medicine & PathologyUniversity of Washington School of MedicineSeattleUSA
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7
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Zheng J, Wu YC, Phillips EH, Wang X, Lee SSY. Increased multiplexity in optical tissue clearing-based 3D immunofluorescence microscopy of the tumor microenvironment by LED photobleaching. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.29.569277. [PMID: 38076864 PMCID: PMC10705380 DOI: 10.1101/2023.11.29.569277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Optical tissue clearing and three-dimensional (3D) immunofluorescence (IF) microscopy have been transforming imaging of the complex tumor microenvironment (TME). However, current 3D IF microscopy has restricted multiplexity; only three or four cellular and non-cellular TME components can be localized in a cleared tumor tissue. Here we report a LED photobleaching method and its application for 3D multiplexed optical mapping of the TME. We built a high-power LED light irradiation device and temperature-controlled chamber for completely bleaching fluorescent signals throughout optically cleared tumor tissues without compromise of tissue and protein antigen integrity. With newly developed tissue mounting and selected region-tracking methods, we established a cyclic workflow involving IF staining, tissue clearing, 3D confocal microscopy, and LED photobleaching. By registering microscope channel images generated through three work cycles, we produced 8-plex image data from individual 400 μm-thick tumor macrosections that visualize various vascular, immune, and cancer cells in the same TME at tissue-wide and cellular levels in 3D. Our method was also validated for quantitative 3D spatial analysis of cellular remodeling in the TME after immunotherapy. These results demonstrate that our LED photobleaching system and its workflow offer a novel approach to increase the multiplexing power of 3D IF microscopy for studying tumor heterogeneity and response to therapy.
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8
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Bishop KW, Barner LAE, Han Q, Baraznenok E, Lan L, Poudel C, Gao G, Serafin RB, Chow SS, Glaser AK, Janowczyk A, Brenes D, Huang H, Miyasato D, True LD, Kang S, Vaughan JC, Liu JT. An end-to-end workflow for non-destructive 3D pathology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.03.551845. [PMID: 37577615 PMCID: PMC10418226 DOI: 10.1101/2023.08.03.551845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Recent advances in 3D pathology offer the ability to image orders-of-magnitude more tissue than conventional pathology while providing a volumetric context that is lacking with 2D tissue sections, all without requiring destructive tissue sectioning. Generating high-quality 3D pathology datasets on a consistent basis is non-trivial, requiring careful attention to many details regarding tissue preparation, imaging, and data/image processing in an iterative process. Here we provide an end-to-end protocol covering all aspects of a 3D pathology workflow (using light-sheet microscopy as an illustrative imaging platform) with sufficient detail to perform well-controlled preclinical and clinical studies. While 3D pathology is compatible with diverse staining protocols and computationally generated color palettes for visual analysis, this protocol will focus on a fluorescent analog of hematoxylin and eosin (H&E), which remains the most common stain for gold-standard diagnostic determinations. We present our guidelines for a broad range of end-users (e.g., biologists, clinical researchers, and engineers) in a simple tutorial format.
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Affiliation(s)
- Kevin W. Bishop
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | | | - Qinghua Han
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Elena Baraznenok
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Lydia Lan
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
- Department of Biology, University of Washington, Seattle, Washington, USA
| | - Chetan Poudel
- Department of Chemistry, University of Washington, Seattle, Washington, USA
| | - Gan Gao
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
| | - Robert B. Serafin
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
| | - Sarah S.L. Chow
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
| | - Adam K. Glaser
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
| | - Andrew Janowczyk
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
- Department of Oncology, Division of Precision Oncology, University Hospital of Geneva, Geneva, Switzerland
- Department of Clinical Pathology, Division of Clinical Pathology, University Hospital of Geneva, Geneva, Switzerland
| | - David Brenes
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
| | - Hongyi Huang
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
| | - Dominie Miyasato
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
| | - Lawrence D. True
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA
| | - Soyoung Kang
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
| | - Joshua C. Vaughan
- Department of Chemistry, University of Washington, Seattle, Washington, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, Washington, USA
| | - Jonathan T.C. Liu
- Department of Mechanical Engineering, University of Washington, Seattle, Washington, USA
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA
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