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      Simple, Reliable Protocol for High-Yield Solubilization of Seedless Amyloid-β Monomer

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          Abstract

          Self-assembly of the amyloid-β (Aβ) peptide to form toxic oligomers and fibrils is a key causal event in the onset of Alzheimer’s disease, and Aβ is the focus of intense research in neuroscience, biophysics, and structural biology aimed at therapeutic development. Due to its rapid self-assembly and extreme sensitivity to aggregation conditions, preparation of seedless, reproducible Aβ solutions is highly challenging, and there are serious ongoing issues with consistency in the literature. In this paper, we use a liquid-phase separation technique, asymmetric flow field-flow fractionation with multiangle light scattering (AF4-MALS), to develop and validate a simple, effective, economical method for re-solubilization and quality control of purified, lyophilized Aβ samples. Our findings were obtained with recombinant peptide but are physicochemical in nature and thus highly relevant to synthetic peptide. We show that much of the variability in the literature stems from the inability of overly mild solvent treatments to produce consistently monomeric preparations and is rectified by a protocol involving high-pH (>12) dissolution, sonication, and rapid freezing to prevent modification. Aβ treated in this manner is chemically stable, can be stored over long timescales at −80 °C, and exhibits remarkably consistent self-assembly behavior when returned to near-neutral pH. These preparations are highly monomeric, seedless, and do not require additional rounds of size exclusion, eliminating the need for this costly procedure and increasing the flexibility of use. We propose that our improved protocol is the simplest, fastest, and most effective way to solubilize Aβ from diverse sources for sensitive self-assembly and toxicity assays.

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          Most cited references101

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            ImageJ2: ImageJ for the next generation of scientific image data

            Background ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software’s ability to handle the requirements of modern science. Results We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called “ImageJ2” in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. Conclusions Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ’s development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1934-z) contains supplementary material, which is available to authorized users.
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              Common structure of soluble amyloid oligomers implies common mechanism of pathogenesis.

              Soluble oligomers are common to most amyloids and may represent the primary toxic species of amyloids, like the Abeta peptide in Alzheimer's disease (AD). Here we show that all of the soluble oligomers tested display a common conformation-dependent structure that is unique to soluble oligomers regardless of sequence. The in vitro toxicity of soluble oligomers is inhibited by oligomer-specific antibody. Soluble oligomers have a unique distribution in human AD brain that is distinct from fibrillar amyloid. These results indicate that different types of soluble amyloid oligomers have a common structure and suggest they share a common mechanism of toxicity.
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                Author and article information

                Journal
                ACS Chem Neurosci
                ACS Chem Neurosci
                cn
                acncdm
                ACS Chemical Neuroscience
                American Chemical Society
                1948-7193
                13 December 2022
                04 January 2023
                : 14
                : 1
                : 53-71
                Affiliations
                [1]School of Biosciences, University of Sheffield , SheffieldS10 2TN, United Kingdom
                Author notes
                Author information
                https://orcid.org/0000-0003-3131-3619
                https://orcid.org/0000-0002-8535-7038
                Article
                10.1021/acschemneuro.2c00411
                9817077
                36512740
                1330d1e9-f82c-4a1b-8594-a180dd8994ed
                © 2022 The Authors. Published by American Chemical Society

                Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 15 July 2022
                : 21 November 2022
                Funding
                Funded by: Biotechnology and Biological Sciences Research Council, doi 10.13039/501100000268;
                Award ID: BB/P002927/1
                Funded by: University of Sheffield, doi 10.13039/501100000858;
                Award ID: NA
                Categories
                Research Article
                Custom metadata
                cn2c00411
                cn2c00411

                Neurosciences
                alzheimer’s disease,amyloidogenesis,protein preparation,flow fractionation,multiangle light scattering,peptides

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