Pub Date : 2024-03-27DOI: 10.1017/s2633903x24000047
Genevieve Buckley, Georg Ramm, S. Trépout
{"title":"GoldDigger and Checkers, computational developments in cryo-scanning transmission electron tomography to improve the quality of reconstructed volumes","authors":"Genevieve Buckley, Georg Ramm, S. Trépout","doi":"10.1017/s2633903x24000047","DOIUrl":"https://doi.org/10.1017/s2633903x24000047","url":null,"abstract":"","PeriodicalId":72371,"journal":{"name":"Biological imaging","volume":"22 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140375810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-15eCollection Date: 2024-01-01DOI: 10.1017/S2633903X24000059
Amit Singer, Ruiyi Yang
In this article, we propose an algorithm for aligning three-dimensional objects when represented as density maps, motivated by applications in cryogenic electron microscopy. The algorithm is based on minimizing the 1-Wasserstein distance between the density maps after a rigid transformation. The induced loss function enjoys a more benign landscape than its Euclidean counterpart and Bayesian optimization is employed for computation. Numerical experiments show improved accuracy and efficiency over existing algorithms on the alignment of real protein molecules. In the context of aligning heterogeneous pairs, we illustrate a potential need for new distance functions.
{"title":"Alignment of density maps in Wasserstein distance.","authors":"Amit Singer, Ruiyi Yang","doi":"10.1017/S2633903X24000059","DOIUrl":"https://doi.org/10.1017/S2633903X24000059","url":null,"abstract":"<p><p>In this article, we propose an algorithm for aligning three-dimensional objects when represented as density maps, motivated by applications in cryogenic electron microscopy. The algorithm is based on minimizing the 1-Wasserstein distance between the density maps after a rigid transformation. The induced loss function enjoys a more benign landscape than its Euclidean counterpart and Bayesian optimization is employed for computation. Numerical experiments show improved accuracy and efficiency over existing algorithms on the alignment of real protein molecules. In the context of aligning heterogeneous pairs, we illustrate a potential need for new distance functions.</p>","PeriodicalId":72371,"journal":{"name":"Biological imaging","volume":"4 ","pages":"e5"},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11016369/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140868372","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 : 2024-03-06DOI: 10.1017/s2633903x24000035
Qinwen Huang, Ye Zhou, Hsuan-Fu Liu, A. Bartesaghi
{"title":"Joint Micrograph Denoising and Protein Localization in Cryo-Electron Microscopy","authors":"Qinwen Huang, Ye Zhou, Hsuan-Fu Liu, A. Bartesaghi","doi":"10.1017/s2633903x24000035","DOIUrl":"https://doi.org/10.1017/s2633903x24000035","url":null,"abstract":"","PeriodicalId":72371,"journal":{"name":"Biological imaging","volume":"41 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140262616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-23eCollection Date: 2024-01-01DOI: 10.1017/S2633903X24000023
Andy Zhang, Oscar Mickelin, Joe Kileel, Eric J Verbeke, Nicholas F Marshall, Marc Aurèle Gilles, Amit Singer
Single-particle cryogenic electron microscopy (cryo-EM) is an imaging technique capable of recovering the high-resolution three-dimensional (3D) structure of biological macromolecules from many noisy and randomly oriented projection images. One notable approach to 3D reconstruction, known as Kam's method, relies on the moments of the two-dimensional (2D) images. Inspired by Kam's method, we introduce a rotationally invariant metric between two molecular structures, which does not require 3D alignment. Further, we introduce a metric between a stack of projection images and a molecular structure, which is invariant to rotations and reflections and does not require performing 3D reconstruction. Additionally, the latter metric does not assume a uniform distribution of viewing angles. We demonstrate the uses of the new metrics on synthetic and experimental datasets, highlighting their ability to measure structural similarity.
单颗粒低温电子显微镜（cryo-EM）是一种成像技术，能够从许多嘈杂和随机定向的投影图像中恢复生物大分子的高分辨率三维（3D）结构。一种著名的三维重建方法，即 Kam 方法，依赖于二维（2D）图像的矩。受 Kam 方法的启发，我们引入了两个分子结构之间的旋转不变度量，它不需要三维对齐。此外，我们还引入了投影图像堆栈与分子结构之间的度量，该度量对旋转和反射不变，无需进行三维重建。此外，后一种度量方法不假定观察角度的均匀分布。我们在合成数据集和实验数据集上演示了新度量方法的应用，突出了它们测量结构相似性的能力。
{"title":"Moment-based metrics for molecules computable from cryogenic electron microscopy images.","authors":"Andy Zhang, Oscar Mickelin, Joe Kileel, Eric J Verbeke, Nicholas F Marshall, Marc Aurèle Gilles, Amit Singer","doi":"10.1017/S2633903X24000023","DOIUrl":"10.1017/S2633903X24000023","url":null,"abstract":"<p><p>Single-particle cryogenic electron microscopy (cryo-EM) is an imaging technique capable of recovering the high-resolution three-dimensional (3D) structure of biological macromolecules from many noisy and randomly oriented projection images. One notable approach to 3D reconstruction, known as Kam's method, relies on the moments of the two-dimensional (2D) images. Inspired by Kam's method, we introduce a rotationally invariant metric between two molecular structures, which does not require 3D alignment. Further, we introduce a metric between a stack of projection images and a molecular structure, which is invariant to rotations and reflections and does not require performing 3D reconstruction. Additionally, the latter metric does not assume a uniform distribution of viewing angles. We demonstrate the uses of the new metrics on synthetic and experimental datasets, highlighting their ability to measure structural similarity.</p>","PeriodicalId":72371,"journal":{"name":"Biological imaging","volume":"4 ","pages":"e3"},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10951804/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140186429","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 : 2024-02-15eCollection Date: 2024-01-01DOI: 10.1017/S2633903X24000011
Joshua A Bull, Eoghan J Mulholland, Simon J Leedham, Helen M Byrne
Imaging platforms for generating highly multiplexed histological images are being continually developed and improved. Significant improvements have also been made in the accuracy of methods for automated cell segmentation and classification. However, less attention has focused on the quantification and analysis of the resulting point clouds, which describe the spatial coordinates of individual cells. We focus here on a particular spatial statistical method, the cross-pair correlation function (cross-PCF), which can identify positive and negative spatial correlation between cells across a range of length scales. However, limitations of the cross-PCF hinder its widespread application to multiplexed histology. For example, it can only consider relations between pairs of cells, and cells must be classified using discrete categorical labels (rather than labeling continuous labels such as stain intensity). In this paper, we present three extensions to the cross-PCF which address these limitations and permit more detailed analysis of multiplex images: topographical correlation maps can visualize local clustering and exclusion between cells; neighbourhood correlation functions can identify colocalization of two or more cell types; and weighted-PCFs describe spatial correlation between points with continuous (rather than discrete) labels. We apply the extended PCFs to synthetic and biological datasets in order to demonstrate the insight that they can generate.
{"title":"Extended correlation functions for spatial analysis of multiplex imaging data.","authors":"Joshua A Bull, Eoghan J Mulholland, Simon J Leedham, Helen M Byrne","doi":"10.1017/S2633903X24000011","DOIUrl":"10.1017/S2633903X24000011","url":null,"abstract":"<p><p>Imaging platforms for generating highly multiplexed histological images are being continually developed and improved. Significant improvements have also been made in the accuracy of methods for automated cell segmentation and classification. However, less attention has focused on the quantification and analysis of the resulting point clouds, which describe the spatial coordinates of individual cells. We focus here on a particular spatial statistical method, the cross-pair correlation function (cross-PCF), which can identify positive and negative spatial correlation between cells across a range of length scales. However, limitations of the cross-PCF hinder its widespread application to multiplexed histology. For example, it can only consider relations between pairs of cells, and cells must be classified using discrete categorical labels (rather than labeling continuous labels such as stain intensity). In this paper, we present three extensions to the cross-PCF which address these limitations and permit more detailed analysis of multiplex images: topographical correlation maps can visualize local clustering and exclusion between cells; neighbourhood correlation functions can identify colocalization of two or more cell types; and weighted-PCFs describe spatial correlation between points with continuous (rather than discrete) labels. We apply the extended PCFs to synthetic and biological datasets in order to demonstrate the insight that they can generate.</p>","PeriodicalId":72371,"journal":{"name":"Biological imaging","volume":"4 ","pages":"e2"},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10951806/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140186428","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 : 2023-12-14DOI: 10.1017/s2633903x23000260
L. Panconi, A. Tansell, A. J. Collins, M. Makarova, D.M. Owen
{"title":"Three-dimensional topology-based analysis segments volumetric and spatiotemporal fluorescence microscopy","authors":"L. Panconi, A. Tansell, A. J. Collins, M. Makarova, D.M. Owen","doi":"10.1017/s2633903x23000260","DOIUrl":"https://doi.org/10.1017/s2633903x23000260","url":null,"abstract":"","PeriodicalId":72371,"journal":{"name":"Biological imaging","volume":"321 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138974166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-20DOI: 10.1017/S2633903X23000259
Sayed Muhammed Rassul, Masahiro Otsu, I. Styles, R. K. Neely, Daniel Fulton
Abstract This study aimed to expand our understanding of myelin basic protein (MBP), a key component of central nervous system myelin, by developing a protocol to track and quantifying individual MBP particles during oligodendrocyte (OL) differentiation. MBP particle directionality, confinement, and diffusion were tracked by rapid TIRF and HILO imaging of Dendra2 tagged MBP in three stages of mouse oligodendroglia: OL precursors, early myelinating OLs, and mature myelinating OLs. The directionality and confinement of MBP particles increased at each stage consistent with progressive transport toward, and recruitment into, emerging myelin structures. Unexpectedly, diffusion data presented a more complex pattern with subpopulations of the most diffusive particles disappearing at the transition between the precursor and early myelinating stage, before reemerging in the membrane sheets of mature OLs. This diversity of particle behaviors, which would be undetectable by conventional ensemble-averaged methods, are consistent with a multifunctional view of MBP involving roles in myelin expansion and compaction.
摘要 本研究旨在通过制定一套方案，在少突胶质细胞（OL）分化过程中跟踪和量化单个 MBP 颗粒，从而扩大我们对中枢神经系统髓鞘的关键成分--髓鞘碱性蛋白（MBP）的了解。通过对小鼠少突胶质细胞三个阶段的 Dendra2 标记 MBP 进行快速 TIRF 和 HILO 成像，跟踪 MBP 粒子的方向性、封闭性和扩散：少突胶质细胞前体、早期髓鞘化少突胶质细胞和成熟髓鞘化少突胶质细胞。在每个阶段，MBP 颗粒的方向性和封闭性都在增加，这与向新生髓鞘结构的渐进运输和招募相一致。出乎意料的是，扩散数据呈现出一种更为复杂的模式，扩散性最强的颗粒亚群在前体和早期髓鞘化阶段之间的过渡时期消失，然后重新出现在成熟 OL 的膜片中。这种颗粒行为的多样性是传统的集合平均方法所无法检测到的，它与 MBP 的多功能观点一致，MBP 在髓鞘扩张和压实中发挥作用。
{"title":"Single-molecule tracking of myelin basic protein during oligodendrocyte differentiation","authors":"Sayed Muhammed Rassul, Masahiro Otsu, I. Styles, R. K. Neely, Daniel Fulton","doi":"10.1017/S2633903X23000259","DOIUrl":"https://doi.org/10.1017/S2633903X23000259","url":null,"abstract":"Abstract This study aimed to expand our understanding of myelin basic protein (MBP), a key component of central nervous system myelin, by developing a protocol to track and quantifying individual MBP particles during oligodendrocyte (OL) differentiation. MBP particle directionality, confinement, and diffusion were tracked by rapid TIRF and HILO imaging of Dendra2 tagged MBP in three stages of mouse oligodendroglia: OL precursors, early myelinating OLs, and mature myelinating OLs. The directionality and confinement of MBP particles increased at each stage consistent with progressive transport toward, and recruitment into, emerging myelin structures. Unexpectedly, diffusion data presented a more complex pattern with subpopulations of the most diffusive particles disappearing at the transition between the precursor and early myelinating stage, before reemerging in the membrane sheets of mature OLs. This diversity of particle behaviors, which would be undetectable by conventional ensemble-averaged methods, are consistent with a multifunctional view of MBP involving roles in myelin expansion and compaction.","PeriodicalId":72371,"journal":{"name":"Biological imaging","volume":"51 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139256643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-13DOI: 10.1017/s2633903x2300020x
Lisa Balsollier, Frédéric Lavancier, Jean Salamero, Charles Kervrann
An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
{"title":"A generative model to simulate spatio-temporal dynamics of biomolecules in cells","authors":"Lisa Balsollier, Frédéric Lavancier, Jean Salamero, Charles Kervrann","doi":"10.1017/s2633903x2300020x","DOIUrl":"https://doi.org/10.1017/s2633903x2300020x","url":null,"abstract":"An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.","PeriodicalId":72371,"journal":{"name":"Biological imaging","volume":"22 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136347193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-13DOI: 10.1017/s2633903x23000235
Madhavi Tippani, Heena R. Divecha, Joseph L. Catallini, Sang Ho Kwon, Lukas M. Weber, Abby Spangler, Andrew E. Jaffe, Thomas M. Hyde, Joel E. Kleinman, Stephanie C. Hicks, Keri Martinowich, Leonardo Collado-Torres, Stephanie C. Page, Kristen R. Maynard
An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
{"title":"<i>VistoSeg</i>: processing utilities for high resolution images for spatially resolved transcriptomics data","authors":"Madhavi Tippani, Heena R. Divecha, Joseph L. Catallini, Sang Ho Kwon, Lukas M. Weber, Abby Spangler, Andrew E. Jaffe, Thomas M. Hyde, Joel E. Kleinman, Stephanie C. Hicks, Keri Martinowich, Leonardo Collado-Torres, Stephanie C. Page, Kristen R. Maynard","doi":"10.1017/s2633903x23000235","DOIUrl":"https://doi.org/10.1017/s2633903x23000235","url":null,"abstract":"An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.","PeriodicalId":72371,"journal":{"name":"Biological imaging","volume":"53 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136347617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-13DOI: 10.1017/s2633903x23000247
Anca-Ioana Grapa, Georgios Efthymiou, Ellen Van Obberghen-Schilling, Laure Blanc-Féraud, Xavier Descombes
An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
{"title":"A spatial statistical framework for the parametric study of fiber networks: application to fibronectin deposition by normal and activated fibroblasts","authors":"Anca-Ioana Grapa, Georgios Efthymiou, Ellen Van Obberghen-Schilling, Laure Blanc-Féraud, Xavier Descombes","doi":"10.1017/s2633903x23000247","DOIUrl":"https://doi.org/10.1017/s2633903x23000247","url":null,"abstract":"An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.","PeriodicalId":72371,"journal":{"name":"Biological imaging","volume":"10 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136348409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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