Pub Date : 2025-02-03DOI: 10.1016/j.yjsbx.2025.100121
ZiJian Bai, Jian Huang
In the single-particle Cryo-EM projection image classification, it is a common practice to apply the Fourier transform to the images and extract rotation-invariant features in the frequency domain. However, this process involves interpolation, which can reduce the accuracy of the results. In contrast, the non-uniform Fourier transform provides more direct and accurate computation of rotation-invariant features without the need for interpolation in the computation process. Leveraging the capabilities of the non-uniform discrete Fourier transform (NUDFT), we have developed an algorithm for the rotation-invariant classification. To highlight its potential and applicability in the field of single-particle Cryo-EM, we conducted a direct comparison with the traditional Fourier transform and other methods, demonstrating the superior performance of the NUDFT.
{"title":"Non-uniform Fourier transform based image classification in single-particle Cryo-EM","authors":"ZiJian Bai, Jian Huang","doi":"10.1016/j.yjsbx.2025.100121","DOIUrl":"10.1016/j.yjsbx.2025.100121","url":null,"abstract":"<div><div>In the single-particle Cryo-EM projection image classification, it is a common practice to apply the Fourier transform to the images and extract rotation-invariant features in the frequency domain. However, this process involves interpolation, which can reduce the accuracy of the results. In contrast, the non-uniform Fourier transform provides more direct and accurate computation of rotation-invariant features without the need for interpolation in the computation process. Leveraging the capabilities of the non-uniform discrete Fourier transform (NUDFT), we have developed an algorithm for the rotation-invariant classification. To highlight its potential and applicability in the field of single-particle Cryo-EM, we conducted a direct comparison with the traditional Fourier transform and other methods, demonstrating the superior performance of the NUDFT.</div></div>","PeriodicalId":17238,"journal":{"name":"Journal of Structural Biology: X","volume":"11 ","pages":"Article 100121"},"PeriodicalIF":3.5,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-21DOI: 10.1016/j.yjsbx.2025.100120
Kenneth D. Carr , Dane Evan D. Zambrano , Connor Weidle , Alex Goodson , Helen E. Eisenach , Harley Pyles , Alexis Courbet , Neil P. King , Andrew J. Borst
Protein purification is essential in protein biochemistry, structural biology, and protein design, enabling the determination of protein structures, the study of biological mechanisms, and the characterization of both natural and de novo designed proteins. However, standard purification strategies often encounter challenges, such as unintended co-purification of contaminants alongside the target protein. This issue is particularly problematic for self-assembling protein nanomaterials, where unexpected geometries may reflect novel assembly states, cross-contamination, or native proteins originating from the expression host. Here, we used an automated structure-to-sequence pipeline to first identify an unknown co-purifying protein found in several purified designed protein samples. By integrating cryo-electron microscopy (Cryo-EM), ModelAngelo’s sequence-agnostic model-building, and Protein BLAST, we identified the contaminant as dihydrolipoamide succinyltransferase (DLST). This identification was validated through comparisons with DLST structures in the Protein Data Bank, AlphaFold 3 predictions based on the DLST sequence from our E. coli expression vector, and traditional biochemical methods. The identification informed subsequent modifications to our purification protocol, which successfully excluded DLST from future preparations. To explore the potential broader utility of this approach, we benchmarked four computational methods for DLST identification across varying resolution ranges. This study demonstrates the successful application of a structure-to-sequence protein identification workflow, integrating Cryo-EM, ModelAngelo, Protein BLAST, and AlphaFold 3 predictions, to identify and ultimately help guide the removal of DLST from sample purification efforts. It highlights the potential of combining Cryo-EM with AI-driven tools for accurate protein identification and addressing purification challenges across diverse contexts in protein science.
{"title":"Protein identification using Cryo-EM and artificial intelligence guides improved sample purification","authors":"Kenneth D. Carr , Dane Evan D. Zambrano , Connor Weidle , Alex Goodson , Helen E. Eisenach , Harley Pyles , Alexis Courbet , Neil P. King , Andrew J. Borst","doi":"10.1016/j.yjsbx.2025.100120","DOIUrl":"10.1016/j.yjsbx.2025.100120","url":null,"abstract":"<div><div>Protein purification is essential in protein biochemistry, structural biology, and protein design, enabling the determination of protein structures, the study of biological mechanisms, and the characterization of both natural and de novo designed proteins. However, standard purification strategies often encounter challenges, such as unintended co-purification of contaminants alongside the target protein. This issue is particularly problematic for self-assembling protein nanomaterials, where unexpected geometries may reflect novel assembly states, cross-contamination, or native proteins originating from the expression host. Here, we used an automated structure-to-sequence pipeline to first identify an unknown co-purifying protein found in several purified designed protein samples. By integrating cryo-electron microscopy (Cryo-EM), ModelAngelo’s sequence-agnostic model-building, and Protein BLAST, we identified the contaminant as dihydrolipoamide succinyltransferase (DLST). This identification was validated through comparisons with DLST structures in the Protein Data Bank, AlphaFold 3 predictions based on the DLST sequence from our E. coli expression vector, and traditional biochemical methods. The identification informed subsequent modifications to our purification protocol, which successfully excluded DLST from future preparations. To explore the potential broader utility of this approach, we benchmarked four computational methods for DLST identification across varying resolution ranges. This study demonstrates the successful application of a structure-to-sequence protein identification workflow, integrating Cryo-EM, ModelAngelo, Protein BLAST, and AlphaFold 3 predictions, to identify and ultimately help guide the<!--> <!-->removal of DLST from sample purification efforts. It highlights the potential of combining Cryo-EM with AI-driven tools for accurate protein identification and addressing purification challenges across diverse contexts in protein science.</div></div>","PeriodicalId":17238,"journal":{"name":"Journal of Structural Biology: X","volume":"11 ","pages":"Article 100120"},"PeriodicalIF":3.5,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Siderophore-mediated iron acquisition is essential for the virulence of Aspergillus fumigatus, a fungus causing life-threatening aspergillosis. Drugs targeting the siderophore biosynthetic pathway could help improve disease management. The transacetylases SidF and SidL generate intermediates for different siderophores in A. fumigatus. A. fumigatus has a yet unidentified transacetylase that complements SidL during iron deficiency in SidL-lacking mutants.
We present the first X-ray structure of SidF, revealing a two-domain architecture with tetrameric assembly. The N-terminal domain contributes to protein solubility and oligomerization, while the C-terminal domain containing the GCN5-related N-acetyltransferase (GNAT) motif is crucial for the enzymatic activity and mediates oligomer formation. Notably, AlphaFold modelling demonstrates structural similarity between SidF and SidL. Enzymatic assays showed that SidF can utilize acetyl-CoA as a donor, previously thought to be a substrate of SidL but not SidF, and selectively uses N5-hydroxy-L-ornithine as an acceptor.
This study elucidates the structure of SidF and reveals its role in siderophore biosynthesis. We propose SidF as the unknown transacetylase complementing SidL activity, highlighting its central role in A. fumigatus siderophore biosynthesis. Investigation of this uncharacterized GNAT protein enhances our understanding of fungal virulence and holds promise for its potential application in developing antifungal therapies.
{"title":"SidF, a dual substrate N5-acetyl-N5-hydroxy-L-ornithine transacetylase involved in Aspergillus fumigatus siderophore biosynthesis","authors":"Thanalai Poonsiri , Jan Stransky , Nicola Demitri , Hubertus Haas , Michele Cianci , Stefano Benini","doi":"10.1016/j.yjsbx.2024.100119","DOIUrl":"10.1016/j.yjsbx.2024.100119","url":null,"abstract":"<div><div>Siderophore-mediated iron acquisition is essential for the virulence of <em>Aspergillus fumigatus</em>, a fungus causing life-threatening aspergillosis. Drugs targeting the siderophore biosynthetic pathway could help improve disease management. The transacetylases SidF and SidL generate intermediates for different siderophores in <em>A. fumigatus</em>. <em>A. fumigatus</em> has a yet unidentified transacetylase that complements SidL during iron deficiency in SidL-lacking mutants.</div><div>We present the first X-ray structure of SidF, revealing a two-domain architecture with tetrameric assembly. The N-terminal domain contributes to protein solubility and oligomerization, while the C-terminal domain containing the GCN5-related N-acetyltransferase (GNAT) motif is crucial for the enzymatic activity and mediates oligomer formation. Notably, AlphaFold modelling demonstrates structural similarity between SidF and SidL. Enzymatic assays showed that SidF can utilize acetyl-CoA as a donor, previously thought to be a substrate of SidL but not SidF, and selectively uses N5-hydroxy-L-ornithine as an acceptor.</div><div>This study elucidates the structure of SidF and reveals its role in siderophore biosynthesis. We propose SidF as the unknown transacetylase complementing SidL activity, highlighting its central role in <em>A. fumigatus</em> siderophore biosynthesis. Investigation of this uncharacterized GNAT protein enhances our understanding of fungal virulence and holds promise for its potential application in developing antifungal therapies.</div></div>","PeriodicalId":17238,"journal":{"name":"Journal of Structural Biology: X","volume":"11 ","pages":"Article 100119"},"PeriodicalIF":3.5,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11751504/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143023943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We investigated several small viral proteins that reside and function in cellular membranes. These proteins belong to the viroporin family because they assemble into ion-conducting oligomers. However, despite forming similar oligomeric structures with analogous functions, these proteins have diverse amino acid sequences. In particular, the amino acid compositions of the proposed channel-forming transmembrane (TM) helices are vastly different—some contain residues (e.g., His, Trp, Asp, Ser) that could facilitate cation transport. Still, other viroporins’ TM helices encompass exclusively hydrophobic residues; therefore, it is difficult to explain their channels’ activity, unless other mechanisms (e.g., involving a negative lipid headgroups and/or membrane destabilization) take place. For this study, we selected the M2, Vpu, E, p13II, p7, and 2B proteins from the influenza A, HIV-1, human T-cell leukemia, hepatitis C, and picorna viruses, respectively. We provide a brief overview of the current knowledge about these proteins’ structures as well as remaining questions about more comprehensive understanding of their structures, conformational dynamics, and function. Finally, we outline strategies to utilize a multi-prong structural and computational approach to overcome current deficiencies in the knowledge about these proteins.
{"title":"Highly versatile small virus-encoded proteins in cellular membranes: A structural perspective on how proteins’ inherent conformational plasticity couples with host membranes’ properties to control cellular processes","authors":"Arvin Saffarian Delkhosh , Elaheh Hadadianpour , Md Majharul Islam, Elka R. Georgieva","doi":"10.1016/j.yjsbx.2024.100117","DOIUrl":"10.1016/j.yjsbx.2024.100117","url":null,"abstract":"<div><div>We investigated several small viral proteins that reside and function in cellular membranes. These proteins belong to the viroporin family because they assemble into ion-conducting oligomers. However, despite forming similar oligomeric structures with analogous functions, these proteins have diverse amino acid sequences. In particular, the amino acid compositions of the proposed channel-forming transmembrane (TM) helices are vastly different—some contain residues (e.g., His, Trp, Asp, Ser) that could facilitate cation transport. Still, other viroporins’ TM helices encompass exclusively hydrophobic residues; therefore, it is difficult to explain their channels’ activity, unless other mechanisms (e.g., involving a negative lipid headgroups and/or membrane destabilization) take place. For this study, we selected the M2, Vpu, E, p13II, p7, and 2B proteins from the influenza A, HIV-1, human T-cell leukemia, hepatitis C, and picorna viruses, respectively. We provide a brief overview of the current knowledge about these proteins’ structures as well as remaining questions about more comprehensive understanding of their structures, conformational dynamics, and function. Finally, we outline strategies to utilize a multi-prong structural and computational approach to overcome current deficiencies in the knowledge about these proteins.</div></div>","PeriodicalId":17238,"journal":{"name":"Journal of Structural Biology: X","volume":"11 ","pages":"Article 100117"},"PeriodicalIF":3.5,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11714672/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142971424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1016/j.yjsbx.2024.100116
{"title":"Editorial by Natalie Reznikov [for Buss et al., “Hierarchical organization of bone in three dimensions: A twist of twists” (2022)]","authors":"","doi":"10.1016/j.yjsbx.2024.100116","DOIUrl":"10.1016/j.yjsbx.2024.100116","url":null,"abstract":"","PeriodicalId":17238,"journal":{"name":"Journal of Structural Biology: X","volume":"10 ","pages":"Article 100116"},"PeriodicalIF":3.5,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1016/j.yjsbx.2024.100112
Gabin de La Bourdonnaye , Martin Marek , Tereza Ghazalova , Jiri Damborsky , Petr Pachl , Jiri Brynda , Veronika Stepankova , Radka Chaloupkova
Fibroblast growth factor 2 (FGF2) is a signaling protein that plays a significant role in tissue development and repair. FGF2 binds to fibroblast growth factor receptors (FGFRs) alongside its co-factor heparin, which protects FGF2 from degradation. The binding between FGF2 and FGFRs induces intracellular signaling pathways such as RAS-MAPK, PI3K-AKT, and STAT. FGF2 has strong potential for application in cell culturing, wound healing, and cosmetics but the potential is severely limited by its low protein stability. The thermostable variant FGF2-STAB was constructed by computer-assisted protein engineering to overcome the natural limitation of FGF2. Previously reported characterization of FGF2-STAB revealed an enhanced ability to induce MAP/ERK signaling while having a lower dependence on heparin when compared with FGF2-wt. Here we report the crystal structure of FGF2-STAB solved at 1.3 Å resolution. Protein stabilization is achieved by newly formed hydrophobic interactions, polar contacts, and one additional hydrogen bond. The overall structure of FGF2-STAB is similar to FGF2-wt and does not reveal information on the experimentally observed lower dependence on heparin. A noticeable difference in flexibility in the receptor binding region can explain the differences in signaling between FGF2-STAB and its wild-type counterpart. Our structural analysis provided molecular insights into the stabilization and unique biological properties of FGF2-STAB.
{"title":"Structural analysis of the stable form of fibroblast growth factor 2 – FGF2-STAB","authors":"Gabin de La Bourdonnaye , Martin Marek , Tereza Ghazalova , Jiri Damborsky , Petr Pachl , Jiri Brynda , Veronika Stepankova , Radka Chaloupkova","doi":"10.1016/j.yjsbx.2024.100112","DOIUrl":"10.1016/j.yjsbx.2024.100112","url":null,"abstract":"<div><div>Fibroblast growth factor 2 (FGF2) is a signaling protein that plays a significant role in tissue development and repair. FGF2 binds to fibroblast growth factor receptors (FGFRs) alongside its co-factor heparin, which protects FGF2 from degradation. The binding between FGF2 and FGFRs induces intracellular signaling pathways such as RAS-MAPK, PI3K-AKT, and STAT. FGF2 has strong potential for application in cell culturing, wound healing, and cosmetics but the potential is severely limited by its low protein stability. The thermostable variant FGF2-STAB was constructed by computer-assisted protein engineering to overcome the natural limitation of FGF2. Previously reported characterization of FGF2-STAB revealed an enhanced ability to induce MAP/ERK signaling while having a lower dependence on heparin when compared with FGF2-wt. Here we report the crystal structure of FGF2-STAB solved at 1.3 Å resolution. Protein stabilization is achieved by newly formed hydrophobic interactions, polar contacts, and one additional hydrogen bond. The overall structure of FGF2-STAB is similar to FGF2-wt and does not reveal information on the experimentally observed lower dependence on heparin. A noticeable difference in flexibility in the receptor binding region can explain the differences in signaling between FGF2-STAB and its wild-type counterpart. Our structural analysis provided molecular insights into the stabilization and unique biological properties of FGF2-STAB.</div></div>","PeriodicalId":17238,"journal":{"name":"Journal of Structural Biology: X","volume":"10 ","pages":"Article 100112"},"PeriodicalIF":3.5,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-21DOI: 10.1016/j.yjsbx.2024.100114
Alexandra N. Birtasu , Utz H. Ermel , Johanna V. Rahm , Anja Seybert , Benjamin Flottmann , Mike Heilemann , Florian Grahammer , Achilleas S. Frangakis
The functioning of vertebrate life relies on renal filtration of surplus fluid and elimination of low-molecular-weight waste products, while keeping serum proteins in the blood. In disease, however, there is leak of serum proteins and tracing them to identify the leaking position within tissue with a nanometer resolution poses a significant challenge. Correlative microscopy integrates the specificity of fluorescent protein labeling into high-resolution electron micrographs. Using chemical tagging of albumin with synthetic fluorophores we achieve protein-specific labeling that preserve their post-embedding fluorescence after high-pressure freezing and freeze-substitution of murine kidney tissue. Using advanced registration techniques for super-resolution correlative light and electron microscopy, we can localize the labeled albumin with a high precision in the x-y plane of electron micrographs and cartograph its distribution. Thereby we can quantify the albumin concentration and measure a linear reduction gradient across the kidney filtration barrier. Our study shows the feasibility of combining different microscopy contrasts for tracing fluorescently labeled protein markers with super resolution in various tissue samples and opens new perspectives for correlative imaging in volume electron microscopy.
{"title":"Localization of albumin with correlative super resolution light- and electron microscopy in the kidney","authors":"Alexandra N. Birtasu , Utz H. Ermel , Johanna V. Rahm , Anja Seybert , Benjamin Flottmann , Mike Heilemann , Florian Grahammer , Achilleas S. Frangakis","doi":"10.1016/j.yjsbx.2024.100114","DOIUrl":"10.1016/j.yjsbx.2024.100114","url":null,"abstract":"<div><div>The functioning of vertebrate life relies on renal filtration of surplus fluid and elimination of low-molecular-weight waste products, while keeping serum proteins in the blood. In disease, however, there is leak of serum proteins and tracing them to identify the leaking position within tissue with a nanometer resolution poses a significant challenge. Correlative microscopy integrates the specificity of fluorescent protein labeling into high-resolution electron micrographs. Using chemical tagging of albumin with synthetic fluorophores we achieve protein-specific labeling that preserve their post-embedding fluorescence after high-pressure freezing and freeze-substitution of murine kidney tissue. Using advanced registration techniques for super-resolution correlative light and electron microscopy, we can localize the labeled albumin with a high precision in the x-y plane of electron micrographs and cartograph its distribution. Thereby we can quantify the albumin concentration and measure a linear reduction gradient across the kidney filtration barrier. Our study shows the feasibility of combining different microscopy contrasts for tracing fluorescently labeled protein markers with super resolution in various tissue samples and opens new perspectives for correlative imaging in volume electron microscopy.</div></div>","PeriodicalId":17238,"journal":{"name":"Journal of Structural Biology: X","volume":"10 ","pages":"Article 100114"},"PeriodicalIF":3.5,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-18DOI: 10.1016/j.yjsbx.2024.100113
Chia-Chun Hsieh, Zi-Jing Lin, Lee-Jene Lai
Cryo-soft X-ray tomography (cryo-SXT) is a newly developed technique for imaging 3D whole cells in nearly native states. Cryo-SXT users require the preparation of numerous cryo-sample grids to use the allocated beamtime to study cellular phenomena under various conditions. Therefore, it is important to promptly prepare cryo-sample grids as efficiently and carefully as possible to minimize ice contamination on the frozen sample grid. In this study, we designed a cryo-multi-grid-box storage system, which includes a shell, funnel holder, and multi-grid-box container. Our system not only increases the number of cryo-sample grids that can be temporarily stored but also reduces the frequency of cryo grid-box container transfers, thus decreasing the probability of forming ice on the grid. We have also applied this system to A549 cryo cell grid preparation. The correlative images from cryo-light microscopy and cryo-SXT showed that limited ice had formed on the grid when preparation was performed using our system. Additionally, 3D images of mitochondria with the lamellar shape of the cristae could be observed in our cryo-SXT results. Our cryo-multi-grid-box storage system can be used for cryo-SXT and cryo-electron tomography (cryo-ET) applications.
低温软 X 射线断层成像（Cryo-SXT）是一种新开发的技术，用于对接近原生状态的三维全细胞进行成像。Cryo-SXT 用户需要制备大量低温样品网格，以利用分配的光束时间研究各种条件下的细胞现象。因此，必须尽可能高效、仔细地及时制备冷冻样品网格，以尽量减少冰对冷冻样品网格的污染。在这项研究中，我们设计了一种低温多栅格盒存储系统，包括外壳、漏斗支架和多栅格盒容器。我们的系统不仅增加了可临时存储的冷冻样本网格数量，还减少了冷冻网格-盒式容器的转移频率，从而降低了网格上结冰的概率。我们还将这一系统应用于 A549 低温细胞网格制备。冷冻光学显微镜和冷冻-SXT 的相关图像显示，在使用我们的系统进行制备时，网格上形成的冰非常有限。此外，冷冻-SXT 结果还能观察到线粒体的三维图像，其嵴呈片状。我们的低温多网格盒存储系统可用于低温-SXT 和低温电子断层扫描（cryo-ET）应用。
{"title":"Minimizing ice contamination during specimen preparation for cryo-soft X-ray tomography and cryo-electron tomography","authors":"Chia-Chun Hsieh, Zi-Jing Lin, Lee-Jene Lai","doi":"10.1016/j.yjsbx.2024.100113","DOIUrl":"10.1016/j.yjsbx.2024.100113","url":null,"abstract":"<div><div>Cryo-soft X-ray tomography (cryo-SXT) is a newly developed technique for imaging 3D whole cells in nearly native states. Cryo-SXT users require the preparation of numerous cryo-sample grids to use the allocated beamtime to study cellular phenomena under various conditions. Therefore, it is important to promptly prepare cryo-sample grids as efficiently and carefully as possible to minimize ice contamination on the frozen sample grid. In this study, we designed a cryo-multi-grid-box storage system, which includes a shell, funnel holder, and multi-grid-box container. Our system not only increases the number of cryo-sample grids that can be temporarily stored but also reduces the frequency of cryo grid-box container transfers, thus decreasing the probability of forming ice on the grid. We have also applied this system to A549 cryo cell grid preparation. The correlative images from cryo-light microscopy and cryo-SXT showed that limited ice had formed on the grid when preparation was performed using our system. Additionally, 3D images of mitochondria with the lamellar shape of the cristae could be observed in our cryo-SXT results. Our cryo-multi-grid-box storage system can be used for cryo-SXT and cryo-electron tomography (cryo-ET) applications.</div></div>","PeriodicalId":17238,"journal":{"name":"Journal of Structural Biology: X","volume":"10 ","pages":"Article 100113"},"PeriodicalIF":3.5,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1016/j.yjsbx.2024.100111
Isha Dev , Sofia Mehmood , Nancy Pleshko , Iyad Obeid , William Querido
Understanding the composition of bone tissue at the submicron level is crucial to elucidate factors contributing to bone disease and fragility. Here, we introduce a novel approach utilizing optical photothermal infrared (O-PTIR) spectroscopy and imaging coupled with machine learning analysis to assess bone tissue composition at 500 nm spatial resolution. This approach was used to evaluate thick bone samples embedded in typical poly(methyl methacrylate) (PMMA) blocks, eliminating the need for cumbersome thin sectioning. We demonstrate the utility of O-PTIR imaging to assess the distribution of bone tissue mineral and protein, as well as to explore the structure-composition relationship surrounding microporosity at a spatial resolution unattainable by conventional infrared imaging modalities. Using bone samples from wildtype (WT) mice and from a mouse model of osteogenesis imperfecta (OIM), we further showcase the application of O-PTIR spectroscopy to quantify mineral content, crystallinity, and carbonate content in spatially defined regions across the cortical bone. Notably, we show that machine learning analysis using support vector machine (SVM) was successful in identifying bone phenotypes (typical in WT, fragile in OIM) based on input of spectral data, with over 86 % of samples correctly identified when using the collagen spectral range. Our findings highlight the potential of O-PTIR spectroscopy and imaging as valuable tools for exploring bone submicron composition.
{"title":"Assessment of submicron bone tissue composition in plastic-embedded samples using optical photothermal infrared (O-PTIR) spectral imaging and machine learning","authors":"Isha Dev , Sofia Mehmood , Nancy Pleshko , Iyad Obeid , William Querido","doi":"10.1016/j.yjsbx.2024.100111","DOIUrl":"10.1016/j.yjsbx.2024.100111","url":null,"abstract":"<div><div>Understanding the composition of bone tissue at the submicron level is crucial to elucidate factors contributing to bone disease and fragility. Here, we introduce a novel approach utilizing optical photothermal infrared (O-PTIR) spectroscopy and imaging coupled with machine learning analysis to assess bone tissue composition at 500 nm spatial resolution. This approach was used to evaluate thick bone samples embedded in typical poly(methyl methacrylate) (PMMA) blocks, eliminating the need for cumbersome thin sectioning. We demonstrate the utility of O-PTIR imaging to assess the distribution of bone tissue mineral and protein, as well as to explore the structure-composition relationship surrounding microporosity at a spatial resolution unattainable by conventional infrared imaging modalities. Using bone samples from wildtype (WT) mice and from a mouse model of osteogenesis imperfecta (OIM), we further showcase the application of O-PTIR spectroscopy to quantify mineral content, crystallinity, and carbonate content in spatially defined regions across the cortical bone. Notably, we show that machine learning analysis using support vector machine (SVM) was successful in identifying bone phenotypes (typical in WT, fragile in OIM) based on input of spectral data, with over 86 % of samples correctly identified when using the collagen spectral range. Our findings highlight the potential of O-PTIR spectroscopy and imaging as valuable tools for exploring bone submicron composition.</div></div>","PeriodicalId":17238,"journal":{"name":"Journal of Structural Biology: X","volume":"10 ","pages":"Article 100111"},"PeriodicalIF":3.5,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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