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Zhou H, Guo Y, Fu T, Peng Y, Chen Z, Cui Y, Guo M, Zhang K, Chen C, Wang Y. Three-Dimensional Label-Free Observing of the Self-Assembled Nanoparticles inside a Single Cell at Nanoscale Resolution. ACS NANO 2024. [PMID: 39001860 DOI: 10.1021/acsnano.4c06095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/15/2024]
Abstract
Understanding the intracellular behavior of nanoparticles (NPs) plays a key role in optimizing the self-assembly performance of nanomedicine. However, conducting the 3D, label-free, quantitative observation of self-assembled NPs within intact single cells remains a substantial challenge in complicated intracellular environments. Here, we propose a deep learning combined synchrotron radiation hard X-ray nanotomography approach to visualize the self-assembled ultrasmall iron oxide (USIO) NPs in a single cell. The method allows us to explore comprehensive information on NPs, such as their distribution, morphology, location, and interaction with cell organelles, and provides quantitative analysis of the heterogeneous size and morphologies of USIO NPs under diverse conditions. This label-free, in situ method provides a tool for precise characterization of intracellular self-assembled NPs to improve the evaluation and design of a bioresponsive nanomedicine.
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Affiliation(s)
- Huige Zhou
- New Cornerstone Science Laboratory, CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Research Unit of Nanoscience and Technology, Chinese Academy of Medical Sciences, Beijing 100021, China
| | - Yuecong Guo
- New Cornerstone Science Laboratory, CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tianyu Fu
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Yufeng Peng
- New Cornerstone Science Laboratory, CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Chinese Academy of Sciences, Beijing 100190, China
| | - Ziwei Chen
- New Cornerstone Science Laboratory, CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yanyan Cui
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China
| | - Mengyu Guo
- New Cornerstone Science Laboratory, CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Chinese Academy of Sciences, Beijing 100190, China
| | - Kai Zhang
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Chunying Chen
- New Cornerstone Science Laboratory, CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Research Unit of Nanoscience and Technology, Chinese Academy of Medical Sciences, Beijing 100021, China
| | - Yaling Wang
- New Cornerstone Science Laboratory, CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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2
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Deshmukh A, Chang K, Cuala J, Vanslembrouck B, Georgia S, Loconte V, White KL. Subcellular Feature-Based Classification of α and β Cells Using Soft X-ray Tomography. Cells 2024; 13:869. [PMID: 38786091 PMCID: PMC11119489 DOI: 10.3390/cells13100869] [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: 04/26/2024] [Revised: 05/14/2024] [Accepted: 05/15/2024] [Indexed: 05/25/2024] Open
Abstract
The dysfunction of α and β cells in pancreatic islets can lead to diabetes. Many questions remain on the subcellular organization of islet cells during the progression of disease. Existing three-dimensional cellular mapping approaches face challenges such as time-intensive sample sectioning and subjective cellular identification. To address these challenges, we have developed a subcellular feature-based classification approach, which allows us to identify α and β cells and quantify their subcellular structural characteristics using soft X-ray tomography (SXT). We observed significant differences in whole-cell morphological and organelle statistics between the two cell types. Additionally, we characterize subtle biophysical differences between individual insulin and glucagon vesicles by analyzing vesicle size and molecular density distributions, which were not previously possible using other methods. These sub-vesicular parameters enable us to predict cell types systematically using supervised machine learning. We also visualize distinct vesicle and cell subtypes using Uniform Manifold Approximation and Projection (UMAP) embeddings, which provides us with an innovative approach to explore structural heterogeneity in islet cells. This methodology presents an innovative approach for tracking biologically meaningful heterogeneity in cells that can be applied to any cellular system.
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Affiliation(s)
- Aneesh Deshmukh
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; (A.D.); (K.C.)
| | - Kevin Chang
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; (A.D.); (K.C.)
| | - Janielle Cuala
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; (A.D.); (K.C.)
- Medical Biophysics Program, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Bieke Vanslembrouck
- Department of Anatomy, School of Medicine, University of California San Francisco, San Francisco, CA 94143, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Senta Georgia
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Valentina Loconte
- Department of Anatomy, School of Medicine, University of California San Francisco, San Francisco, CA 94143, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Kate L. White
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; (A.D.); (K.C.)
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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3
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Coale TH, Loconte V, Turk-Kubo KA, Vanslembrouck B, Mak WKE, Cheung S, Ekman A, Chen JH, Hagino K, Takano Y, Nishimura T, Adachi M, Le Gros M, Larabell C, Zehr JP. Nitrogen-fixing organelle in a marine alga. Science 2024; 384:217-222. [PMID: 38603509 DOI: 10.1126/science.adk1075] [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: 12/19/2023] [Accepted: 02/22/2024] [Indexed: 04/13/2024]
Abstract
Symbiotic interactions were key to the evolution of chloroplast and mitochondria organelles, which mediate carbon and energy metabolism in eukaryotes. Biological nitrogen fixation, the reduction of abundant atmospheric nitrogen gas (N2) to biologically available ammonia, is a key metabolic process performed exclusively by prokaryotes. Candidatus Atelocyanobacterium thalassa, or UCYN-A, is a metabolically streamlined N2-fixing cyanobacterium previously reported to be an endosymbiont of a marine unicellular alga. Here we show that UCYN-A has been tightly integrated into algal cell architecture and organellar division and that it imports proteins encoded by the algal genome. These are characteristics of organelles and show that UCYN-A has evolved beyond endosymbiosis and functions as an early evolutionary stage N2-fixing organelle, or "nitroplast."
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Affiliation(s)
- Tyler H Coale
- Ocean Sciences Department, University of California, Santa Cruz, CA, USA
| | - Valentina Loconte
- Department of Anatomy, School of Medicine, University of California, San Francisco, CA, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Kendra A Turk-Kubo
- Ocean Sciences Department, University of California, Santa Cruz, CA, USA
| | - Bieke Vanslembrouck
- Department of Anatomy, School of Medicine, University of California, San Francisco, CA, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | | | - Shunyan Cheung
- Institute of Marine Biology and Center of Excellence for the Oceans, National Taiwan Ocean University, Keelung, Taiwan
| | - Axel Ekman
- Department of Anatomy, School of Medicine, University of California, San Francisco, CA, USA
| | - Jian-Hua Chen
- Department of Anatomy, School of Medicine, University of California, San Francisco, CA, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Kyoko Hagino
- Marine Core Research Institute, Kochi University, Nankoku, Kochi, Japan
| | - Yoshihito Takano
- Marine Core Research Institute, Kochi University, Nankoku, Kochi, Japan
| | - Tomohiro Nishimura
- Fisheries Technology Institute, Japan Fisheries Research and Education Agency, Hatsukaichi, Hiroshima, Japan
- Laboratory of Aquatic Environmental Science, Faculty of Agriculture and Marine Science, Kochi University, Nankoku, Kochi, Japan
| | - Masao Adachi
- Laboratory of Aquatic Environmental Science, Faculty of Agriculture and Marine Science, Kochi University, Nankoku, Kochi, Japan
| | - Mark Le Gros
- Department of Anatomy, School of Medicine, University of California, San Francisco, CA, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Carolyn Larabell
- Department of Anatomy, School of Medicine, University of California, San Francisco, CA, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Jonathan P Zehr
- Ocean Sciences Department, University of California, Santa Cruz, CA, USA
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4
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Tang Q, Yin D, Liu Y, Zhang J, Guan Y, Kong H, Wang Y, Zhang X, Li J, Wang L, Hu J, Cai X, Zhu Y. Clickable X-ray Nanoprobes for Nanoscopic Bioimaging of Cellular Structures. JACS AU 2024; 4:893-902. [PMID: 38559738 PMCID: PMC10976567 DOI: 10.1021/jacsau.4c00056] [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: 01/18/2024] [Revised: 02/05/2024] [Accepted: 02/07/2024] [Indexed: 04/04/2024]
Abstract
Synchrotron-based X-ray microscopy (XRM) has garnered widespread attention from researchers due to its high spatial resolution and excellent energy (element) resolution. Existing molecular probes suitable for XRM include immune probes and genetic labeling probes, enabling the precise imaging of various biological targets within cells. However, immune labeling techniques are prone to cross-interference between antigens and antibodies. Genetic labeling technologies have limited systems that allow express markers independently, and moreover, genetically encoded labels based on catalytic polymerization lack a fixed morphology. When applied to cell imaging, this can result in reduced localization accuracy due to the diffusion of labels within the cells. Therefore, both techniques face challenges in simultaneously labeling multiple biotargets within cells and achieving high-precision imaging. In this work, we applied the click reaction and developed a third category of imaging probes suitable for XRM, termed clickable X-ray nanoprobes (Click-XRN). Click-XRN consists of two components: an X-ray-sensitive multicolor imaging module and a particle-size-controllable morphology module. Efficient identification of intra- and extracellular biotargets is achieved through click reactions between the probe and biomolecules. Click-XRN possesses a controllable particle size, and its loading of various metal ions provides distinctive signals for imaging under XRM. Based on this, we optimized the imaging energy of Click-XRN with different particle sizes, enabling single-color and two-color imaging of the cell membrane, cell nucleus, and mitochondria with nanoscale spatial nanometers. Our work provides a potent molecular tool for investigating cellular activities through XRM.
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Affiliation(s)
- Qiaowei Tang
- Institute
of Materiobiology, College of Science, Shanghai
University, Shanghai 200444, China
- Xiangfu
Laboratory, Jiashan 314102, China
| | - Dapeng Yin
- Division
of Physical Biology, CAS Key Laboratory of Interfacial Physics and
Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 201800, China
| | - Yubo Liu
- Division
of Physical Biology, CAS Key Laboratory of Interfacial Physics and
Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 201800, China
| | - Jichao Zhang
- Shanghai
Synchrotron Radiation Facility (SSRF), Shanghai
Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201204, China
| | - Yong Guan
- National
Synchrotron Radiation Laboratory, University
of Science and Technology of China, Hefei 230029, China
| | - Huating Kong
- Shanghai
Synchrotron Radiation Facility (SSRF), Shanghai
Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201204, China
| | - Yiliu Wang
- Division
of Physical Biology, CAS Key Laboratory of Interfacial Physics and
Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 201800, China
| | - Xiangzhi Zhang
- Shanghai
Synchrotron Radiation Facility (SSRF), Shanghai
Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201204, China
| | - Jiang Li
- Institute
of Materiobiology, College of Science, Shanghai
University, Shanghai 200444, China
- Division
of Physical Biology, CAS Key Laboratory of Interfacial Physics and
Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 201800, China
| | - Lihua Wang
- Institute
of Materiobiology, College of Science, Shanghai
University, Shanghai 200444, China
- Division
of Physical Biology, CAS Key Laboratory of Interfacial Physics and
Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 201800, China
| | - Jun Hu
- Institute
of Materiobiology, College of Science, Shanghai
University, Shanghai 200444, China
- Division
of Physical Biology, CAS Key Laboratory of Interfacial Physics and
Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 201800, China
| | - Xiaoqing Cai
- Shanghai
Synchrotron Radiation Facility (SSRF), Shanghai
Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201204, China
| | - Ying Zhu
- Institute
of Materiobiology, College of Science, Shanghai
University, Shanghai 200444, China
- Division
of Physical Biology, CAS Key Laboratory of Interfacial Physics and
Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 201800, China
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5
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Cao M, Wang Y, Wang L, Zhang K, Guan Y, Guo Y, Chen C. In situ label-free X-ray imaging for visualizing the localization of nanomedicines and subcellular architecture in intact single cells. Nat Protoc 2024; 19:30-59. [PMID: 37957402 DOI: 10.1038/s41596-023-00902-y] [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: 10/24/2022] [Accepted: 08/10/2023] [Indexed: 11/15/2023]
Abstract
Understanding the intracellular behaviors of nanomedicines and morphology variation of subcellular architecture impacted by nanomaterial-biology (nano-bio) interactions could help guide the safe-by-design, manufacturing and evaluation of nanomedicines for clinical translation. The in situ and label-free analysis of nano-bio interactions in intact single cells at nanoscale remains challenging. We developed an approach based on X-ray microscopy to directly visualize the 2D or 3D intracellular distribution without labeling at nanometer resolution and analyze the chemical transformation of nanomedicines in situ. Here, we describe an optimized workflow for cell sample preparation, beamline selection, data acquisition and analysis. With several model bionanomaterials as examples, we analyze the localization of nanomedicines in various primary blood cells, macrophages, dendritic cells, monocytes and cancer cells, as well as the morphology of some organelles with soft and hard X-rays. Our protocol has been successfully implemented at three beamline facilities: 4W1A of Beijing Synchrotron Radiation Facility, BL08U1A of Shanghai Synchrotron Radiation Facility and BL07W of the National Synchrotron Radiation Laboratory. This protocol can be completed in ~2-5 d, depending on the cell types, their incubation times with nanomaterials and the selected X-ray beamline. The protocol enables the in situ analysis of the varieties of metal-containing nanomaterials, visualization of intracellular endocytosis, distribution and excretion and corresponding subcellular morphological variation influenced by nanomedicines in cell lines or primary cells by using this universal and robust platform. The results facilitate the understanding of the true principle and mechanism underlying the nano-bio interaction.
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Affiliation(s)
- Mingjing Cao
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing, China
| | - Yaling Wang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing, China
| | - Liming Wang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing, China
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
| | - Kai Zhang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
| | - Yong Guan
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, China
| | - Yuecong Guo
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing, China
| | - Chunying Chen
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing, China.
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China.
- GBA National Institute for Nanotechnology Innovation, Guangzhou, China.
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6
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McLaughlin MR, Weaver SA, Syed F, Evans-Molina C. Advanced Imaging Techniques for the Characterization of Subcellular Organelle Structure in Pancreatic Islet β Cells. Compr Physiol 2023; 14:5243-5267. [PMID: 38158370 DOI: 10.1002/cphy.c230002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Type 2 diabetes (T2D) affects more than 32.3 million individuals in the United States, creating an economic burden of nearly $966 billion in 2021. T2D results from a combination of insulin resistance and inadequate insulin secretion from the pancreatic β cell. However, genetic and physiologic data indicate that defects in β cell function are the chief determinant of whether an individual with insulin resistance will progress to a diagnosis of T2D. The subcellular organelles of the insulin secretory pathway, including the endoplasmic reticulum, Golgi apparatus, and secretory granules, play a critical role in maintaining the heavy biosynthetic burden of insulin production, processing, and secretion. In addition, the mitochondria enable the process of insulin release by integrating the metabolism of nutrients into energy output. Advanced imaging techniques are needed to determine how changes in the structure and composition of these organelles contribute to the loss of insulin secretory capacity in the β cell during T2D. Several microscopy techniques, including electron microscopy, fluorescence microscopy, and soft X-ray tomography, have been utilized to investigate the structure-function relationship within the β cell. In this overview article, we will detail the methodology, strengths, and weaknesses of each approach. © 2024 American Physiological Society. Compr Physiol 14:5243-5267, 2024.
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Affiliation(s)
- Madeline R McLaughlin
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Staci A Weaver
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- The Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Farooq Syed
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana, USA
- The Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Carmella Evans-Molina
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- The Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Anatomy, Cell Biology, and Physiology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Roudebush VA Medical Center, Indianapolis, Indiana, USA
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7
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Shrestha P, LaManna JM, Fahy KF, Kim P, Lee C, Lee JK, Baltic E, Jacobson DL, Hussey DS, Bazylak A. Simultaneous multimaterial operando tomography of electrochemical devices. SCIENCE ADVANCES 2023; 9:eadg8634. [PMID: 37939178 PMCID: PMC10631724 DOI: 10.1126/sciadv.adg8634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 10/06/2023] [Indexed: 11/10/2023]
Abstract
The performance of electrochemical energy devices, such as fuel cells and batteries, is dictated by intricate physiochemical processes within. To better understand and rationally engineer these processes, we need robust operando characterization tools that detect and distinguish multiple interacting components/interfaces in high contrast. Here, we uniquely combine dual-modality tomography (simultaneous neutron and x-ray tomography) and advanced image processing (iterative reconstruction and metal artifact reduction) for high-contrast multimaterial imaging, with signal and contrast enhancements of up to 10 and 48 times, respectively, compared to conventional single-modality imaging. Targeted development and application of these methods to electrochemical devices allow us to resolve operando distributions of six interacting fuel cell components (including void space) with the highest reported pairwise contrast for simultaneous yet decoupled spatiotemporal characterization of component morphology and hydration. Such high-contrast tomography ushers in key gold standards for operando electrochemical characterization, with broader applicability to numerous multimaterial systems.
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Affiliation(s)
- Pranay Shrestha
- Bazylak Group, Department of Mechanical & Industrial Engineering, Faculty of Applied Science and Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Jacob M. LaManna
- Physical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Kieran F. Fahy
- Bazylak Group, Department of Mechanical & Industrial Engineering, Faculty of Applied Science and Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Pascal Kim
- Bazylak Group, Department of Mechanical & Industrial Engineering, Faculty of Applied Science and Engineering, University of Toronto, Toronto, Ontario, Canada
| | - ChungHyuk Lee
- Bazylak Group, Department of Mechanical & Industrial Engineering, Faculty of Applied Science and Engineering, University of Toronto, Toronto, Ontario, Canada
- Department of Chemical Engineering, Toronto Metropolitan University, Toronto, Ontario, Canada
| | - Jason K. Lee
- Bazylak Group, Department of Mechanical & Industrial Engineering, Faculty of Applied Science and Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Elias Baltic
- Physical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - David L. Jacobson
- Physical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Daniel S. Hussey
- Physical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Aimy Bazylak
- Bazylak Group, Department of Mechanical & Industrial Engineering, Faculty of Applied Science and Engineering, University of Toronto, Toronto, Ontario, Canada
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8
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Golyshev SA, Kazakov EP, Kireev II, Reunov DG, Malyshev IV. Soft X-ray Microscopy in Cell Biology: Current Status, Contributions and Prospects. Acta Naturae 2023; 15:32-43. [PMID: 38234603 PMCID: PMC10790358 DOI: 10.32607/actanaturae.26551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 11/27/2023] [Indexed: 01/19/2024] Open
Abstract
The recent advances achieved in microscopy technology have led to a significant breakthrough in biological research. Super-resolution fluorescent microscopy now allows us to visualize subcellular structures down to the pin-pointing of the single molecules in them, while modern electron microscopy has opened new possibilities in the study of protein complexes in their native, intracellular environment at near-atomic resolution. Nonetheless, both fluorescent and electron microscopy have remained beset by their principal shortcomings: the reliance on labeling procedures and severe sample volume limitations, respectively. Soft X-ray microscopy is a candidate method that can compensate for the shortcomings of both technologies by making possible observation of the entirety of the cellular interior without chemical fixation and labeling with an isotropic resolution of 40-70 nm. This will thus bridge the resolution gap between light and electron microscopy (although this gap is being narrowed, it still exists) and resolve the issue of compatibility with the former, and possibly in the near future, the latter methods. This review aims to assess the current state of soft X-ray microscopy and its impact on our understanding of the subcellular organization. It also attempts to look into the future of X-ray microscopy, particularly as relates to its seamless integration into the cell biology toolkit.
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Affiliation(s)
- S. A. Golyshev
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119992 Russian Federation
| | - E. P. Kazakov
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119992 Russian Federation
| | - I. I. Kireev
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119992 Russian Federation
| | - D. G. Reunov
- Institute of Physics of Microstructures RAS, Nizhny Novgorod, 603950 Russian Federation
| | - I. V. Malyshev
- Institute of Physics of Microstructures RAS, Nizhny Novgorod, 603950 Russian Federation
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9
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Loconte V, Singla J, Li A, Chen JH, Ekman A, McDermott G, Sali A, Gros ML, White KL, Larabell CA. Soft X-ray Tomography for Mapping and Quantifying Intracellular Organelle Interactions. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2023; 29:1181. [PMID: 37613466 DOI: 10.1093/micmic/ozad067.607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Affiliation(s)
- Valentina Loconte
- iHuman Institute, School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- Department of Anatomy, University of California San Francisco, San Francisco, CA, United States
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Jitin Singla
- Department of Chemistry, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA, United States
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
| | - Angdi Li
- iHuman Institute, School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Jian-Hua Chen
- Department of Anatomy, University of California San Francisco, San Francisco, CA, United States
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Axel Ekman
- Department of Anatomy, University of California San Francisco, San Francisco, CA, United States
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Gerry McDermott
- Department of Anatomy, University of California San Francisco, San Francisco, CA, United States
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Science, Department of Pharmaceutical Chemistry, California Institute of Quantitative Bioscience, University of California San Francisco, San Francisco, CA, United States
| | - Mark Le Gros
- Department of Anatomy, University of California San Francisco, San Francisco, CA, United States
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Kate L White
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
- Department of Chemistry, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA, United States
| | - Carolyn A Larabell
- Department of Anatomy, University of California San Francisco, San Francisco, CA, United States
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
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10
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Loconte V, Chen JH, Vanslembrouck B, Ekman AA, McDermott G, Gros MAL, Larabell CA. The Role of Soft X-ray Tomography in Generating Whole-cell Models. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2023; 29:1170. [PMID: 37613169 DOI: 10.1093/micmic/ozad067.600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Affiliation(s)
- Valentina Loconte
- Department of Anatomy, University of California San Francisco, San Francisco, California, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
- National Center for X-ray Tomography, Advanced Light Source, Berkeley, California, USA
| | - Jian-Hua Chen
- Department of Anatomy, University of California San Francisco, San Francisco, California, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
- National Center for X-ray Tomography, Advanced Light Source, Berkeley, California, USA
| | - Bieke Vanslembrouck
- Department of Anatomy, University of California San Francisco, San Francisco, California, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
- National Center for X-ray Tomography, Advanced Light Source, Berkeley, California, USA
| | - Axel A Ekman
- National Center for X-ray Tomography, Advanced Light Source, Berkeley, California, USA
| | - Gerry McDermott
- Department of Anatomy, University of California San Francisco, San Francisco, California, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
- National Center for X-ray Tomography, Advanced Light Source, Berkeley, California, USA
| | - Mark A Le Gros
- Department of Anatomy, University of California San Francisco, San Francisco, California, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
- National Center for X-ray Tomography, Advanced Light Source, Berkeley, California, USA
| | - Carolyn A Larabell
- Department of Anatomy, University of California San Francisco, San Francisco, California, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
- National Center for X-ray Tomography, Advanced Light Source, Berkeley, California, USA
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11
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Deshmukh A, Loconte V, White KL. Quantitative Structural Mapping of Insulin Vesicle Maturation in Beta Cells. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2023; 29:1166. [PMID: 37613637 DOI: 10.1093/micmic/ozad067.597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Affiliation(s)
- Aneesh Deshmukh
- Department of Chemistry, University of Southern California, Los Angeles, CA, USA
| | - Valentina Loconte
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkely, CA, USA
| | - Kate L White
- Department of Chemistry, University of Southern California, Los Angeles, CA, USA
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12
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de Klerk E, Xiao Y, Emfinger CH, Keller MP, Berrios DI, Loconte V, Ekman AA, White KL, Cardone RL, Kibbey RG, Attie AD, Hebrok M. Loss of ZNF148 enhances insulin secretion in human pancreatic β cells. JCI Insight 2023; 8:157572. [PMID: 37288664 PMCID: PMC10393241 DOI: 10.1172/jci.insight.157572] [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: 12/14/2021] [Accepted: 04/05/2023] [Indexed: 06/09/2023] Open
Abstract
Insulin secretion from pancreatic β cells is essential to the maintenance of glucose homeostasis. Defects in this process result in diabetes. Identifying genetic regulators that impair insulin secretion is crucial for the identification of novel therapeutic targets. Here, we show that reduction of ZNF148 in human islets, and its deletion in stem cell-derived β cells (SC-β cells), enhances insulin secretion. Transcriptomics of ZNF148-deficient SC-β cells identifies increased expression of annexin and S100 genes whose proteins form tetrameric complexes involved in regulation of insulin vesicle trafficking and exocytosis. ZNF148 in SC-β cells prevents translocation of annexin A2 from the nucleus to its functional place at the cell membrane via direct repression of S100A16 expression. These findings point to ZNF148 as a regulator of annexin-S100 complexes in human β cells and suggest that suppression of ZNF148 may provide a novel therapeutic strategy to enhance insulin secretion.
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Affiliation(s)
| | - Yini Xiao
- UCSF Diabetes Center, UCSF, San Francisco, California, USA
| | - Christopher H Emfinger
- Department of Biochemistry, University of Wisconsin-Madison, DeLuca Biochemistry Laboratories, Madison, Wisconsin, USA
| | - Mark P Keller
- Department of Biochemistry, University of Wisconsin-Madison, DeLuca Biochemistry Laboratories, Madison, Wisconsin, USA
| | | | - Valentina Loconte
- Department of Anatomy, School of Medicine, UCSF, San Francisco, California, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
- National Center for X-ray Tomography, Advanced Light Source, Berkeley, California, USA
| | - Axel A Ekman
- National Center for X-ray Tomography, Advanced Light Source, Berkeley, California, USA
| | - Kate L White
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, California, USA
| | - Rebecca L Cardone
- Department of Internal Medicine (Endocrinology), Yale University, New Haven, Connecticut, USA
| | - Richard G Kibbey
- Department of Internal Medicine (Endocrinology), Yale University, New Haven, Connecticut, USA
| | - Alan D Attie
- Departments of Biochemistry, Chemistry, and Medicine, University of Wisconsin-Madison, DeLuca Biochemistry Laboratories, Madison, Wisconsin, USA
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13
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Loconte V, Chen J, Vanslembrouck B, Ekman AA, McDermott G, Le Gros MA, Larabell CA. Soft X-ray tomograms provide a structural basis for whole-cell modeling. FASEB J 2023; 37:e22681. [PMID: 36519968 PMCID: PMC10107707 DOI: 10.1096/fj.202200253r] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 11/13/2022] [Accepted: 11/21/2022] [Indexed: 12/23/2022]
Abstract
Developing in silico models that accurately reflect a whole, functional cell is an ongoing challenge in biology. Current efforts bring together mathematical models, probabilistic models, visual representations, and data to create a multi-scale description of cellular processes. A realistic whole-cell model requires imaging data since it provides spatial constraints and other critical cellular characteristics that are still impossible to obtain by calculation alone. This review introduces Soft X-ray Tomography (SXT) as a powerful imaging technique to visualize and quantify the mesoscopic (~25 nm spatial scale) organelle landscape in whole cells. SXT generates three-dimensional reconstructions of cellular ultrastructure and provides a measured structural framework for whole-cell modeling. Combining SXT with data from disparate technologies at varying spatial resolutions provides further biochemical details and constraints for modeling cellular mechanisms. We conclude, based on the results discussed here, that SXT provides a foundational dataset for a broad spectrum of whole-cell modeling experiments.
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Affiliation(s)
- Valentina Loconte
- Department of AnatomyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Molecular Biophysics and Integrated Bioimaging DivisionLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
- National Center for X‐ray TomographyAdvanced Light SourceBerkeleyCaliforniaUSA
| | - Jian‐Hua Chen
- Department of AnatomyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Molecular Biophysics and Integrated Bioimaging DivisionLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
- National Center for X‐ray TomographyAdvanced Light SourceBerkeleyCaliforniaUSA
| | - Bieke Vanslembrouck
- Department of AnatomyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Molecular Biophysics and Integrated Bioimaging DivisionLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
- National Center for X‐ray TomographyAdvanced Light SourceBerkeleyCaliforniaUSA
| | - Axel A. Ekman
- National Center for X‐ray TomographyAdvanced Light SourceBerkeleyCaliforniaUSA
| | - Gerry McDermott
- Department of AnatomyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Molecular Biophysics and Integrated Bioimaging DivisionLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
- National Center for X‐ray TomographyAdvanced Light SourceBerkeleyCaliforniaUSA
| | - Mark A. Le Gros
- Department of AnatomyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Molecular Biophysics and Integrated Bioimaging DivisionLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
- National Center for X‐ray TomographyAdvanced Light SourceBerkeleyCaliforniaUSA
| | - Carolyn A. Larabell
- Department of AnatomyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Molecular Biophysics and Integrated Bioimaging DivisionLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
- National Center for X‐ray TomographyAdvanced Light SourceBerkeleyCaliforniaUSA
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14
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Li A, Zhang S, Loconte V, Liu Y, Ekman A, Thompson GJ, Sali A, Stevens RC, White K, Singla J, Sun L. An intensity-based post-processing tool for 3D instance segmentation of organelles in soft X-ray tomograms. PLoS One 2022; 17:e0269887. [PMID: 36048824 PMCID: PMC9436087 DOI: 10.1371/journal.pone.0269887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 05/29/2022] [Indexed: 11/29/2022] Open
Abstract
Investigating the 3D structures and rearrangements of organelles within a single cell is critical for better characterizing cellular function. Imaging approaches such as soft X-ray tomography have been widely applied to reveal a complex subcellular organization involving multiple inter-organelle interactions. However, 3D segmentation of organelle instances has been challenging despite its importance in organelle characterization. Here we propose an intensity-based post-processing tool to identify and separate organelle instances. Our tool separates sphere-like (insulin vesicle) and columnar-shaped organelle instances (mitochondrion) based on the intensity of raw tomograms, semantic segmentation masks, and organelle morphology. We validate our tool using synthetic tomograms of organelles and experimental tomograms of pancreatic β-cells to separate insulin vesicle and mitochondria instances. As compared to the commonly used connected regions labeling, watershed, and watershed + Gaussian filter methods, our tool results in improved accuracy in identifying organelles in the synthetic tomograms and an improved description of organelle structures in β-cell tomograms. In addition, under different experimental treatment conditions, significant changes in volumes and intensities of both insulin vesicle and mitochondrion are observed in our instance results, revealing their potential roles in maintaining normal β-cell function. Our tool is expected to be applicable for improving the instance segmentation of other images obtained from different cell types using multiple imaging modalities.
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Affiliation(s)
- Angdi Li
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Shuning Zhang
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Valentina Loconte
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Yan Liu
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Axel Ekman
- Department of Anatomy, University of California San Francisco, San Francisco, CA, United States of America
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | | | - Andrej Sali
- California Institute for Quantitative Biosciences, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, United States of America
| | - Raymond C. Stevens
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- Department of Biological Sciences, Bridge Institute, University of Southern California, Los Angeles, CA, United States of America
- Department of Chemistry, Bridge Institute, University of Southern California, Los Angeles, CA, United States of America
| | - Kate White
- Department of Chemistry, Bridge Institute, University of Southern California, Los Angeles, CA, United States of America
- * E-mail: (KW); (JS); (LS)
| | - Jitin Singla
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
- * E-mail: (KW); (JS); (LS)
| | - Liping Sun
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- * E-mail: (KW); (JS); (LS)
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15
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Integrative modeling of the cell. Acta Biochim Biophys Sin (Shanghai) 2022; 54:1213-1221. [PMID: 36017893 PMCID: PMC9909318 DOI: 10.3724/abbs.2022115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
A whole-cell model represents certain aspects of the cell structure and/or function. Due to the high complexity of the cell, an integrative modeling approach is often taken to utilize all available information including experimental data, prior knowledge and prior models. In this review, we summarize an emerging workflow of whole-cell modeling into five steps: (i) gather information; (ii) represent the modeled system into modules; (iii) translate input information into scoring function; (iv) sample the whole-cell model; (v) validate and interpret the model. In particular, we propose the integrative modeling of the cell by combining available (whole-cell) models to maximize the accuracy, precision, and completeness. In addition, we list quantitative predictions of various aspects of cell biology from existing whole-cell models. Moreover, we discuss the remaining challenges and future directions, and highlight the opportunity to establish an integrative spatiotemporal multi-scale whole-cell model based on a community approach.
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16
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Cao M, Zhang K, Zhang S, Wang Y, Chen C. Advanced Light Source Analytical Techniques for Exploring the Biological Behavior and Fate of Nanomedicines. ACS CENTRAL SCIENCE 2022; 8:1063-1080. [PMID: 36032763 PMCID: PMC9413437 DOI: 10.1021/acscentsci.2c00680] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Indexed: 05/09/2023]
Abstract
Exploration of the biological behavior and fate of nanoparticles, as affected by the nanomaterial-biology (nano-bio) interaction, has become progressively critical for guiding the rational design and optimization of nanomedicines to minimize adverse effects, support clinical translation, and aid in evaluation by regulatory agencies. Because of the complexity of the biological environment and the dynamic variations in the bioactivity of nanomedicines, in-situ, label-free analysis of the transport and transformation of nanomedicines has remained a challenge. Recent improvements in optics, detectors, and light sources have allowed the expansion of advanced light source (ALS) analytical technologies to dig into the underexplored behavior and fate of nanomedicines in vivo. It is increasingly important to further develop ALS-based analytical technologies with higher spatial and temporal resolution, multimodal data fusion, and intelligent prediction abilities to fully unlock the potential of nanomedicines. In this Outlook, we focus on several selected ALS analytical technologies, including imaging and spectroscopy, and provide an overview of the emerging opportunities for their applications in the exploration of the biological behavior and fate of nanomedicines. We also discuss the challenges and limitations faced by current approaches and tools and the expectations for the future development of advanced light sources and technologies. Improved ALS imaging and spectroscopy techniques will accelerate a profound understanding of the biological behavior of new nanomedicines. Such advancements are expected to inspire new insights into nanomedicine research and promote the development of ALS capabilities and methods more suitable for nanomedicine evaluation with the goal of clinical translation.
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Affiliation(s)
- Mingjing Cao
- CAS
Key Laboratory for Biomedical Effects of Nanomedicines and Nanosafety
& CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China
| | - Kai Zhang
- Beijing
Synchrotron Radiation Facility, Institute
of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Shuhan Zhang
- CAS
Key Laboratory for Biomedical Effects of Nanomedicines and Nanosafety
& CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China
| | - Yaling Wang
- CAS
Key Laboratory for Biomedical Effects of Nanomedicines and Nanosafety
& CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China
- The
GBA National Institute for Nanotechnology Innovation, Guangzhou 510700, China
| | - Chunying Chen
- CAS
Key Laboratory for Biomedical Effects of Nanomedicines and Nanosafety
& CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China
- The
GBA National Institute for Nanotechnology Innovation, Guangzhou 510700, China
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17
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Nanometer-Resolution Imaging of Living Cells Using Soft X-ray Contact Microscopy. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Soft X-ray microscopy is a powerful technique for imaging cells with nanometer resolution in their native state without chemical fixation, staining, or sectioning. The studies performed in several laboratories have demonstrated the potential of applying this technique for imaging the internal structures of intact cells. However, it is currently used mainly on synchrotrons with restricted access. Moreover, the operation of these instruments and the associated sample-preparation protocols require interdisciplinary and highly specialized personnel, limiting their wide application in practice. This is why soft X-ray microscopy is not commonly used in biological laboratories as an imaging tool. Thus, a laboratory-based and user-friendly soft X-ray contact microscope would facilitate the work of biologists. A compact, desk-top laboratory setup for soft X-ray contact microscopy (SXCM) based on a laser-plasma soft X-ray source, which can be used in any biological laboratory, together with several applications for biological imaging, are described. Moreover, the perspectives of the correlation of SXCM with other super-resolution imaging techniques based on the current literature are discussed.
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18
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Li A, Zhang X, Singla J, White K, Loconte V, Hu C, Zhang C, Li S, Li W, Francis JP, Wang C, Sali A, Sun L, He X, Stevens RC. Auto-segmentation and time-dependent systematic analysis of mesoscale cellular structure in β-cells during insulin secretion. PLoS One 2022; 17:e0265567. [PMID: 35324950 PMCID: PMC8947144 DOI: 10.1371/journal.pone.0265567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 03/03/2022] [Indexed: 02/07/2023] Open
Abstract
The mesoscale description of the subcellular organization informs about cellular mechanisms in disease state. However, applications of soft X-ray tomography (SXT), an important approach for characterizing organelle organization, are limited by labor-intensive manual segmentation. Here we report a pipeline for automated segmentation and systematic analysis of SXT tomograms. Our approach combines semantic and first-applied instance segmentation to produce separate organelle masks with high Dice and Recall indexes, followed by analysis of organelle localization based on the radial distribution function. We demonstrated this technique by investigating the organization of INS-1E pancreatic β-cell organization under different treatments at multiple time points. Consistent with a previous analysis of a similar dataset, our results revealed the impact of glucose stimulation on the localization and molecular density of insulin vesicles and mitochondria. This pipeline can be extended to SXT tomograms of any cell type to shed light on the subcellular rearrangements under different drug treatments.
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Affiliation(s)
- Angdi Li
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiangyi Zhang
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Jitin Singla
- Department of Biological Sciences, Bridge Institute, University of Southern California, Los Angeles, CA, United States of America
| | - Kate White
- Department of Biological Sciences, Bridge Institute, University of Southern California, Los Angeles, CA, United States of America
| | - Valentina Loconte
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Chuanyang Hu
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Chuyu Zhang
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Shuailin Li
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Weimin Li
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - John Paul Francis
- Department of Computer Science, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA, United States of America
| | - Chenxi Wang
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Andrej Sali
- California Institute for Quantitative Biosciences, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, United States of America
| | - Liping Sun
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- * E-mail: (LS); (XH); (RCS)
| | - Xuming He
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
- Shanghai Engineering Research Center of Intelligent Vision and Imaging, Shanghai, China
- * E-mail: (LS); (XH); (RCS)
| | - Raymond C. Stevens
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- Department of Biological Sciences, Bridge Institute, University of Southern California, Los Angeles, CA, United States of America
- * E-mail: (LS); (XH); (RCS)
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19
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Loconte V, Singla J, Li A, Chen JH, Ekman A, McDermott G, Sali A, Le Gros M, White KL, Larabell CA. Soft X-ray tomography to map and quantify organelle interactions at the mesoscale. Structure 2022; 30:510-521.e3. [PMID: 35148829 PMCID: PMC9013509 DOI: 10.1016/j.str.2022.01.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 12/04/2021] [Accepted: 01/17/2022] [Indexed: 12/11/2022]
Abstract
Inter-organelle interactions are a vital part of normal cellular function; however, these have proven difficult to quantify due to the range of scales encountered in cell biology and the throughput limitations of traditional imaging approaches. Here, we demonstrate that soft X-ray tomography (SXT) can be used to rapidly map ultrastructural reorganization and inter-organelle interactions in intact cells. SXT takes advantage of the naturally occurring, differential X-ray absorption of the carbon-rich compounds in each organelle. Specifically, we use SXT to map the spatiotemporal evolution of insulin vesicles and their co-localization and interaction with mitochondria in pancreatic β cells during insulin secretion and in response to different stimuli. We quantify changes in the morphology, biochemical composition, and relative position of mitochondria and insulin vesicles. These findings highlight the importance of a comprehensive and unbiased mapping at the mesoscale to characterize cell reorganization that would be difficult to detect with other existing methodologies.
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Affiliation(s)
- Valentina Loconte
- iHuman Institute, School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Jitin Singla
- Department of Chemistry, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Angdi Li
- iHuman Institute, School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Jian-Hua Chen
- Department of Anatomy, University of California San Francisco, San Francisco, CA 94143, USA; Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Axel Ekman
- Department of Anatomy, University of California San Francisco, San Francisco, CA 94143, USA; Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Gerry McDermott
- Department of Anatomy, University of California San Francisco, San Francisco, CA 94143, USA; Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Science, Department of Pharmaceutical Chemistry, California Institute of Quantitative Bioscience, University of California San Francisco, San Francisco, CA 94158, USA
| | - Mark Le Gros
- Department of Anatomy, University of California San Francisco, San Francisco, CA 94143, USA; Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Kate L White
- Department of Chemistry, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA.
| | - Carolyn A Larabell
- Department of Anatomy, University of California San Francisco, San Francisco, CA 94143, USA; Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
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20
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Integrative structural modelling and visualisation of a cellular organelle. QRB DISCOVERY 2022. [PMID: 37529283 PMCID: PMC10392685 DOI: 10.1017/qrd.2022.10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Abstract
Models of insulin secretory vesicles from pancreatic beta cells have been created using the cellPACK suite of tools to research, curate, construct and visualise the current state of knowledge. The model integrates experimental information from proteomics, structural biology, cryoelectron microscopy and X-ray tomography, and is used to generate models of mature and immature vesicles. A new method was developed to generate a confidence score that reconciles inconsistencies between three available proteomes using expert annotations of cellular localisation. The models are used to simulate soft X-ray tomograms, allowing quantification of features that are observed in experimental tomograms, and in turn, allowing interpretation of X-ray tomograms at the molecular level.
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21
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A new visual design language for biological structures in a cell. Structure 2022; 30:485-497.e3. [DOI: 10.1016/j.str.2022.01.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 09/28/2021] [Accepted: 01/04/2022] [Indexed: 01/16/2023]
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22
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Loconte V, White KL. The use of soft X-ray tomography to explore mitochondrial structure and function. Mol Metab 2021; 57:101421. [PMID: 34942399 PMCID: PMC8829759 DOI: 10.1016/j.molmet.2021.101421] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 11/22/2021] [Accepted: 12/15/2021] [Indexed: 11/25/2022] Open
Abstract
Background Mitochondria are cellular organelles responsible for energy production, and dysregulation of the mitochondrial network is associated with many disease states. To fully characterize the mitochondrial network's structure and function, a three-dimensional whole cell mapping technique is required. Scope of review This review highlights the use of soft X-ray tomography (SXT) as a relatively high-throughput approach to quantify mitochondrial structure and function under multiple cellular conditions. Major conclusions The use of SXT opens the door for mapping cellular rearrangements during critical processes such as insulin secretion, stem cell differentiation, or disease progression. SXT provides unique information such as biochemical compositions or molecular densities of organelles and allows for unbiased, label-free imaging of intact whole cells. Mapping mitochondria in the context of the near-native cellular environment will reveal more information regarding mitochondrial network functions within the cell. Soft X-ray tomography (SXT) generates 3D organelle maps of intact cells. 3D maps reveal the positions of mitochondria and their molecular densities. SXT can be used to quantify and compare organelle contacts between conditions. SXT is unbiased imaging that identifies the contents of subcellular neighborhoods. SXT provides an exciting path for exploring metabolic dysfunction.
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Affiliation(s)
- Valentina Loconte
- Department of Anatomy, School of Medicine, UCSF, San Francisco, California, CA 94143; Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Kate L White
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA.
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23
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Ast J, Broichhagen J, Hodson DJ. Reagents and models for detecting endogenous GLP1R and GIPR. EBioMedicine 2021; 74:103739. [PMID: 34911028 PMCID: PMC8669301 DOI: 10.1016/j.ebiom.2021.103739] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 11/12/2021] [Accepted: 11/23/2021] [Indexed: 01/18/2023] Open
Abstract
Glucagon-like peptide-1 receptor (GLP1R) agonists target the GLP1R, whereas dual GLP1R/ gastric inhibitory polypeptide receptor (GIPR) agonists target both the GLP1R and GIPR. Despite the importance of these drug classes for the treatment of diabetes and obesity, still very little is known about the localization of GLP1R and GIPR themselves. Complicating matters is the low abundance of GLP1R and GIPR mRNA/protein, as well as a lack of specific and validated reagents for their detection. Without knowing where GLP1R and GIPR are located, it is difficult to propose mechanisms of action in the various target organs, and whether this is indirect or direct. In the current review, we will explain the steps needed to properly validate reagents for endogenous GLP1R/GIPR detection, describe the available approaches to visualize GLP1R/GIPR, and provide an update on the state-of-art. The overall aim is to provide a reference resource for researchers interested in GLP1R and GIPR signaling.
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Affiliation(s)
- Julia Ast
- Institute of Metabolism and Systems Research (IMSR), Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK; Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, UK
| | | | - David J Hodson
- Institute of Metabolism and Systems Research (IMSR), Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK; Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, UK.
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24
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Loconte V, Chen JH, Cortese M, Ekman A, Le Gros MA, Larabell C, Bartenschlager R, Weinhardt V. Using soft X-ray tomography for rapid whole-cell quantitative imaging of SARS-CoV-2-infected cells. CELL REPORTS METHODS 2021; 1:100117. [PMID: 34729550 PMCID: PMC8552653 DOI: 10.1016/j.crmeth.2021.100117] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 09/10/2021] [Accepted: 10/22/2021] [Indexed: 02/08/2023]
Abstract
High-resolution and rapid imaging of host cell ultrastructure can generate insights toward viral disease mechanism, for example for a severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. Here, we employ full-rotation soft X-ray tomography (SXT) to examine organelle remodeling induced by SARS-CoV-2 at the whole-cell level with high spatial resolution and throughput. Most of the current SXT systems suffer from a restricted field of view due to use of flat sample supports and artifacts due to missing data. In this approach using cylindrical sample holders, a full-rotation tomogram of human lung epithelial cells is performed in less than 10 min. We demonstrate the potential of SXT imaging by visualizing aggregates of SARS-CoV-2 virions and virus-induced intracellular alterations. This rapid whole-cell imaging approach allows us to visualize the spatiotemporal changes of cellular organelles upon viral infection in a quantitative manner.
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Affiliation(s)
- Valentina Loconte
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Department of Anatomy, University of California, San Francisco, San Francisco, CA, USA
| | - Jian-Hua Chen
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Department of Anatomy, University of California, San Francisco, San Francisco, CA, USA
| | - Mirko Cortese
- Department of Infectious Diseases, Molecular Virology Heidelberg University, Heidelberg, Germany
| | - Axel Ekman
- Department of Anatomy, University of California, San Francisco, San Francisco, CA, USA
| | - Mark A. Le Gros
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Department of Anatomy, University of California, San Francisco, San Francisco, CA, USA
| | - Carolyn Larabell
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Department of Anatomy, University of California, San Francisco, San Francisco, CA, USA
| | - Ralf Bartenschlager
- Department of Infectious Diseases, Molecular Virology Heidelberg University, Heidelberg, Germany
- German Center for Infection Research, Heidelberg Partner Site, Heidelberg, Germany
- Division Virus-Associated Carcinogenesis, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Venera Weinhardt
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Centre for Organismal Studies, Heidelberg University, Heidelberg, Germany
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25
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Raveh B, Sun L, White KL, Sanyal T, Tempkin J, Zheng D, Bharath K, Singla J, Wang C, Zhao J, Li A, Graham NA, Kesselman C, Stevens RC, Sali A. Bayesian metamodeling of complex biological systems across varying representations. Proc Natl Acad Sci U S A 2021; 118:e2104559118. [PMID: 34453000 PMCID: PMC8536362 DOI: 10.1073/pnas.2104559118] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Comprehensive modeling of a whole cell requires an integration of vast amounts of information on various aspects of the cell and its parts. To divide and conquer this task, we introduce Bayesian metamodeling, a general approach to modeling complex systems by integrating a collection of heterogeneous input models. Each input model can in principle be based on any type of data and can describe a different aspect of the modeled system using any mathematical representation, scale, and level of granularity. These input models are 1) converted to a standardized statistical representation relying on probabilistic graphical models, 2) coupled by modeling their mutual relations with the physical world, and 3) finally harmonized with respect to each other. To illustrate Bayesian metamodeling, we provide a proof-of-principle metamodel of glucose-stimulated insulin secretion by human pancreatic β-cells. The input models include a coarse-grained spatiotemporal simulation of insulin vesicle trafficking, docking, and exocytosis; a molecular network model of glucose-stimulated insulin secretion signaling; a network model of insulin metabolism; a structural model of glucagon-like peptide-1 receptor activation; a linear model of a pancreatic cell population; and ordinary differential equations for systemic postprandial insulin response. Metamodeling benefits from decentralized computing, while often producing a more accurate, precise, and complete model that contextualizes input models as well as resolves conflicting information. We anticipate Bayesian metamodeling will facilitate collaborative science by providing a framework for sharing expertise, resources, data, and models, as exemplified by the Pancreatic β-Cell Consortium.
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Affiliation(s)
- Barak Raveh
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190416, Israel
| | - Liping Sun
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
| | - Kate L White
- Department of Biological Sciences, Bridge Institute, University of Southern California, Los Angeles, CA 90089
| | - Tanmoy Sanyal
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
| | - Jeremy Tempkin
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
| | - Dongqing Zheng
- Mork Family Department of Chemical Engineering and Materials Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089
| | - Kala Bharath
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
| | - Jitin Singla
- Department of Biological Sciences, Bridge Institute, University of Southern California, Los Angeles, CA 90089
- Epstein Department of Industrial and Systems Engineering, The Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089
- Information Science Institute, The Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089
| | - Chenxi Wang
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Jihui Zhao
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
| | - Angdi Li
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Nicholas A Graham
- Mork Family Department of Chemical Engineering and Materials Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089
| | - Carl Kesselman
- Epstein Department of Industrial and Systems Engineering, The Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089
- Information Science Institute, The Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089
| | - Raymond C Stevens
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
- Department of Biological Sciences, Bridge Institute, University of Southern California, Los Angeles, CA 90089
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158;
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158
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26
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Zhang X, Carter SD, Singla J, White KL, Butler PC, Stevens RC, Jensen GJ. Visualizing insulin vesicle neighborhoods in β cells by cryo-electron tomography. SCIENCE ADVANCES 2020; 6:eabc8258. [PMID: 33298442 PMCID: PMC7725471 DOI: 10.1126/sciadv.abc8258] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Accepted: 10/22/2020] [Indexed: 05/04/2023]
Abstract
Subcellular neighborhoods, comprising specific ratios of organelles and proteins, serve a multitude of biological functions and are of particular importance in secretory cells. However, the role of subcellular neighborhoods in insulin vesicle maturation is poorly understood. Here, we present single-cell multiple distinct tomogram acquisitions of β cells for in situ visualization of distinct subcellular neighborhoods that are involved in the insulin vesicle secretory pathway. We propose that these neighborhoods play an essential role in the specific function of cellular material. In the regions where we observed insulin vesicles, a measurable increase in both the fraction of cellular volume occupied by vesicles and the average size (diameter) of the vesicles was apparent as sampling moved from the area near the nucleus toward the plasma membrane. These findings describe the important role of the nanometer-scale organization of subcellular neighborhoods on insulin vesicle maturation.
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Affiliation(s)
- Xianjun Zhang
- Department of Biological Sciences, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Stephen D Carter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Jitin Singla
- Department of Biological Sciences, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Kate L White
- Department of Biological Sciences, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Peter C Butler
- Larry Hillblom Islet Research Center, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Raymond C Stevens
- Department of Biological Sciences, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA.
- Department of Chemistry, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Grant J Jensen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
- Howard Hughes Medical Institute (HHMI), California Institute of Technology, Pasadena, CA 91125, USA
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