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Suresh K, Dahal E, Badano A. Synthetic β-sheets mimicking fibrillar and oligomeric structures for evaluation of spectral X-ray scattering technique for biomarker quantification. Cell Biosci 2024; 14:26. [PMID: 38374092 PMCID: PMC10877803 DOI: 10.1186/s13578-024-01208-6] [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: 10/19/2023] [Accepted: 01/26/2024] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND Archetypical cross-β spines sharpen the boundary between functional and pathological proteins including β-amyloid, tau, α-synuclein and transthyretin are linked to many debilitating human neurodegenerative and non-neurodegenerative amyloidoses. An increased focus on development of pathogenic β-sheet specific fluid and imaging structural biomarkers and conformation-specific monoclonal antibodies in targeted therapies has been recently observed. Identification and quantification of pathogenic oligomers remain challenging for existing neuroimaging modalities. RESULTS We propose two artificial β-sheets which can mimic the nanoscopic structural characteristics of pathogenic oligomers and fibrils for evaluating the performance of a label free, X-ray based biomarker detection and quantification technique. Highly similar structure with elliptical cross-section and parallel cross-β motif is observed among recombinant α-synuclein fibril, Aβ-42 fibril and artificial β-sheet fibrils. We then use these β-sheet models to assess the performance of spectral small angle X-ray scattering (sSAXS) technique for detecting β-sheet structures. sSAXS showed quantitatively accurate detection of antiparallel, cross-β artificial oligomers from a tissue mimicking environment and significant distinction between different oligomer packing densities such as diffuse and dense packings. CONCLUSION The proposed synthetic β-sheet models mimicked the nanoscopic structural characteristics of β-sheets of fibrillar and oligomeric states of Aβ and α-synuclein based on the ATR-FTIR and SAXS data. The tunability of β-sheet proportions and shapes of structural motifs, and the low-cost of these β-sheet models can become useful test materials for evaluating β-sheet or amyloid specific biomarkers in a wide range of neurological diseases. By using the proposed synthetic β-sheet models, our study indicates that the sSAXS has potential to evaluate different stages of β-sheet-enriched structures including oligomers of pathogenic proteins.
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Affiliation(s)
- Karthika Suresh
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, 20993, USA.
| | - Eshan Dahal
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, 20993, USA
| | - Aldo Badano
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, 20993, USA
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Omori NE, Bobitan AD, Vamvakeros A, Beale AM, Jacques SDM. Recent developments in X-ray diffraction/scattering computed tomography for materials science. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2023; 381:20220350. [PMID: 37691470 PMCID: PMC10493554 DOI: 10.1098/rsta.2022.0350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 07/17/2023] [Indexed: 09/12/2023]
Abstract
X-ray diffraction/scattering computed tomography (XDS-CT) methods are a non-destructive class of chemical imaging techniques that have the capacity to provide reconstructions of sample cross-sections with spatially resolved chemical information. While X-ray diffraction CT (XRD-CT) is the most well-established method, recent advances in instrumentation and data reconstruction have seen greater use of related techniques like small angle X-ray scattering CT and pair distribution function CT. Additionally, the adoption of machine learning techniques for tomographic reconstruction and data analysis are fundamentally disrupting how XDS-CT data is processed. The following narrative review highlights recent developments and applications of XDS-CT with a focus on studies in the last five years. This article is part of the theme issue 'Exploring the length scales, timescales and chemistry of challenging materials (Part 2)'.
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Affiliation(s)
- Naomi E. Omori
- Finden Limited, Merchant House, 5 East St Helens Street,Abingdon OX14 5EG, UK
| | - Antonia D. Bobitan
- Finden Limited, Merchant House, 5 East St Helens Street,Abingdon OX14 5EG, UK
- Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK
- Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxon OX11 0FA, UK
| | - Antonis Vamvakeros
- Finden Limited, Merchant House, 5 East St Helens Street,Abingdon OX14 5EG, UK
- Dyson School of Design Engineering, Imperial College London, London SW7 2DB, UK
| | - Andrew M. Beale
- Finden Limited, Merchant House, 5 East St Helens Street,Abingdon OX14 5EG, UK
- Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK
- Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxon OX11 0FA, UK
| | - Simon D. M. Jacques
- Finden Limited, Merchant House, 5 East St Helens Street,Abingdon OX14 5EG, UK
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Aleid A, Alhussaini K, Nisar M. Coherent scatter X-ray imaging of plastic–titanium targets. Radiat Phys Chem Oxf Engl 1993 2023. [DOI: 10.1016/j.radphyschem.2022.110582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
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Chavez T, Roberts EJ, Zwart PH, Hexemer A. A comparison of deep-learning-based inpainting techniques for experimental X-ray scattering. J Appl Crystallogr 2022; 55:1277-1288. [PMID: 36249508 PMCID: PMC9533742 DOI: 10.1107/s1600576722007105] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 07/10/2022] [Indexed: 11/10/2022] Open
Abstract
The implementation is proposed of image inpainting techniques for the reconstruction of gaps in experimental X-ray scattering data. The proposed methods use deep learning neural network architectures, such as convolutional autoencoders, tunable U-Nets, partial convolution neural networks and mixed-scale dense networks, to reconstruct the missing information in experimental scattering images. In particular, the recovered pixel intensities are evaluated against their corresponding ground-truth values using the mean absolute error and the correlation coefficient metrics. The results demonstrate that the proposed methods achieve better performance than traditional inpainting algorithms such as biharmonic functions. Overall, tunable U-Net and mixed-scale dense network architectures achieved the best reconstruction performance among all the tested algorithms, with correlation coefficient scores greater than 0.9980.
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Affiliation(s)
- Tanny Chavez
- Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Eric J. Roberts
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Center for Advanced Mathematics for Energy Research Applications, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Petrus H. Zwart
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Center for Advanced Mathematics for Energy Research Applications, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Berkeley Synchrotron Infrared Structural Biology Program, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Alexander Hexemer
- Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Center for Advanced Mathematics for Energy Research Applications, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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Beam orientation optimization for coherent X-ray scattering from distributed deep targets. Biomed Eng Online 2021; 20:92. [PMID: 34526019 PMCID: PMC8442418 DOI: 10.1186/s12938-021-00928-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 08/29/2021] [Indexed: 11/21/2022] Open
Abstract
Background Amyloid deposits in the temporal and frontal lobes in patients with Alzheimer’s disease make them potential targets to aid in early diagnosis. Recently, spectral small-angle X-ray scattering techniques have been proposed for interrogating deep targets such as amyloid plaques. Results We describe an optimization approach for the orientation of beams for deep target characterization. The model predicts the main features of scattering profiles from targets with varying shape, size and location. We found that increasing target size introduced additional smearing due to location uncertainty, and incidence angle affected the scattering profile by altering the path length or effective target size. For temporal and frontal lobe targets, beam effectiveness varied up to 2 orders of magnitude. Conclusions Beam orientation optimization might allow for patient-specific optimal paths for improved signal characterization.
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Fatafta H, Samantray S, Sayyed-Ahmad A, Coskuner-Weber O, Strodel B. Molecular simulations of IDPs: From ensemble generation to IDP interactions leading to disorder-to-order transitions. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2021; 183:135-185. [PMID: 34656328 DOI: 10.1016/bs.pmbts.2021.06.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/26/2023]
Abstract
Intrinsically disordered proteins (IDPs) lack a well-defined three-dimensional structure but do exhibit some dynamical and structural ordering. The structural plasticity of IDPs indicates that entropy-driven motions are crucial for their function. Many IDPs undergo function-related disorder-to-order transitions upon by their interaction with specific binding partners. Approaches that are based on both experimental and theoretical tools enable the biophysical characterization of IDPs. Molecular simulations provide insights into IDP structural ensembles and disorder-to-order transition mechanisms. However, such studies depend strongly on the chosen force field parameters and simulation techniques. In this chapter, we provide an overview of IDP characteristics, review all-atom force fields recently developed for IDPs, and present molecular dynamics-based simulation methods that allow IDP ensemble generation as well as the characterization of disorder-to-order transitions. In particular, we introduce metadynamics, replica exchange molecular dynamics simulations, and also kinetic models resulting from Markov State modeling, and provide various examples for the successful application of these simulation methods to IDPs.
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Affiliation(s)
- Hebah Fatafta
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, Jülich, Germany
| | - Suman Samantray
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, Jülich, Germany; AICES Graduate School, RWTH Aachen University, Aachen, Germany
| | | | - Orkid Coskuner-Weber
- Molecular Biotechnology, Turkish-German University, Sahinkaya Caddesi, Istanbul, Turkey
| | - Birgit Strodel
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, Jülich, Germany; Institute of Theoretical and Computational Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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Dahal E, Ghammraoui B, Ye M, Smith JC, Badano A. Label-free X-ray estimation of brain amyloid burden. Sci Rep 2020; 10:20505. [PMID: 33239703 PMCID: PMC7689528 DOI: 10.1038/s41598-020-77554-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 11/12/2020] [Indexed: 12/02/2022] Open
Abstract
Amyloid plaque deposits in the brain are indicative of Alzheimer’s and other diseases. Measurements of brain amyloid burden in small animals require laborious post-mortem histological analysis or resource-intensive, contrast-enhanced imaging techniques. We describe a label-free method based on spectral small-angle X-ray scattering with a polychromatic beam for in vivo estimation of brain amyloid burden. Our findings comparing 5XFAD versus wild-type mice correlate well with histology, showing promise for a fast and practical in vivo technique.
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Affiliation(s)
- Eshan Dahal
- Division of Imaging, Diagnostics and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, 20993, USA.,Fischell Department of Bioengineering, University of Maryland, College Park, MD, 20742, USA
| | - Bahaa Ghammraoui
- Division of Imaging, Diagnostics and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, 20993, USA
| | - Meijun Ye
- Division of Biomedical Physics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, 20993, USA
| | - J Carson Smith
- School of Public Health, University of Maryland, College Park, MD, 20742, USA
| | - Aldo Badano
- Division of Imaging, Diagnostics and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, 20993, USA. .,Fischell Department of Bioengineering, University of Maryland, College Park, MD, 20742, USA.
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Breedlove S, Crentsil J, Dahal E, Badano A. Small-angle X-ray scattering characterization of a [Formula: see text]-amyloid model in phantoms. BMC Res Notes 2020; 13:128. [PMID: 32131889 PMCID: PMC7057533 DOI: 10.1186/s13104-020-04969-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 02/21/2020] [Indexed: 12/19/2022] Open
Abstract
OBJECTIVE We present a method to prepare an amyloid model at scalable quantities for phantom studies to evaluate small-angle x-ray scattering systems for amyloid detection. Two amyloid models were made from a plasma protein with and without heating. Both models mimic the [Formula: see text]-sheet structure of the [Formula: see text]-amyloid ([Formula: see text]) plaques in Alzheimer's disease. Amyloid detection is based on the distinct peaks in the scattering signature of the [Formula: see text]-sheet structure. We characterized the amyloid models using a spectral small-angle x-ray scattering (sSAXS) prototype with samples in a plastic syringe and within a cylindrical polymethyl methacrylate (PMMA) phantom. RESULTS sSAXS data show that we can detect the scattering peaks characteristic of amyloid [Formula: see text]-sheet structure in both models around 6 and 13 [Formula: see text]. The [Formula: see text] model prepared without heating provides a stronger signal in the PMMA phantom. The methods described can be used to prepare models in sufficiently large quantities and used in samples with different packing density to assess the performance of [Formula: see text] quantification systems.
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Affiliation(s)
- Sophya Breedlove
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD USA
- Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, PA USA
| | - Jasson Crentsil
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD USA
- Department of Chemical, Biochemical and Environmental Engineering, University of Maryland Baltimore County, Baltimore, MD USA
| | - Eshan Dahal
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD USA
- Fischell Department of Bioengineering, University of Maryland, College Park, MD USA
| | - Aldo Badano
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD USA
- Fischell Department of Bioengineering, University of Maryland, College Park, MD USA
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