Quantitative fibre analysis of single-molecule localization microscopy data

Peters, Ruby; Griffié, Juliette; Burn, Garth L.; Williamson, David J.; Owen, Dylan M.

doi:10.1038/s41598-018-28691-5

Cited by 28 publications

(31 citation statements)

References 41 publications

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“…This is distinct from localizing individual fluorophores through single molecule localization microscopy (SMLM), the Delaunay triangulation (DT) of those fluorophore localizations, or subgraphs of the DT such as the Euclidean Minimum Spanning Tree (Xie et al, 2016; Kittisopikul et al, 2019). Extracting information about fibrous lamin structures from SMLM data would require additional analysis not directly realizable from SMLM localizations or their graphs (Peters et al, 2018; Kittisopikul et al, 2019). Our analysis of lamin fibers as employed here has been purpose built and validated for use in dense structures such as lamin meshworks with complex junctions (Kittisopikul et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Computational analysis of lamin isoform interactions with nuclear pore complexes

Kittisopikul

Shimi

Tatli

et al. 2020

Preprint

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Nuclear lamin isoforms assemble into fibrous meshworks within the nuclear lamina 16 (NL) where they are associated with nuclear pore complexes (NPCs). Although the lamins and 17NPCs are major components of the nuclear envelope (NE), little is known about their structural 18 relationships. We used 3D structured illumination microscopy (3D-SIM) and sub-pixel image 19 analysis to show that NPCs are closely associated with lamin fibers in mouse embryonic 20 fibroblasts (MEFs). When lamin A/C (LA/C) or lamin B1 (LB1) are removed by gene knockout, the 21 NPCs retained their association and redistributed with the resulting enlarged lamin meshworks. 22 Cryo-ET revealed that more LA/C than LB1 fibers contacted the nucleoplasmic ring of NPCs. 23 Knockdown of the outer ring nucleoporin ELYS induced NPC clusters that excluded LA/C fibers. 24 Knockdown of the basket nucleoporin TPR reduced the size of LA/C, LB1, and LB2 meshworks 25 while retaining their close association with NPCs. NUP153 knockdown reduced LA/C and B2 26 meshwork size in wild type (WT) MEFs and caused NPC clustering in nuclei lacking LB1. Therefore, 27 lamins and nucleoporins act together to maintain the organization and distribution of lamin 28 meshworks and NPCs. 29 30 teins, the nuclear lamins, and nuclear pore complexes (NPCs) (Aebi et al., 1986; Fisher et al., 1986; 33 Goldman et al., 1986; McKeon et al., 1986). The four lamin isoforms LA, LC, LB1, and LB2 are mainly 34 associated with the inner nuclear membrane where they assemble into discrete meshworks of 35 fibers composing the nuclear lamina (NL). The NPCs penetrate the NE forming transport passage-36 ways delineated by the fusion of the inner and outer nuclear membranes, thereby allowing for 37 bidirectional transport across the NE. They are composed of multiple copies of 30 proteins known 38 1 of 27Manuscript to be submitted to eLife as nucleoporins (Beck and Hurt, 2016). Both the nuclear lamins and NPC structures are closely 39 associated with chromatin at the nuclear periphery (Guelen et al., 2008; Peric-Hupkes et al., 2010; 40 Ibarra and Hetzer, 2015). 41 The lamins are classified as A-type (LA, LC) and B-type (LB1, LB2). LA and LC are derived from 42 the Lmna gene by alternative splicing (Lin and Worman, 1993), whereas LB1 and LB2 are encoded 43 81 ity of the four lamin fiber meshworks and NPCs located within a thin layer at the nuclear surface. 82The results of our analyses demonstrate that NPCs are closely associated with lamin fibers. At 83 higher resolution cryo-ET confirms that both LA/C and LB1 filaments interact closely with the NPCs 84 at the nucleoplasmic ring. Targeted disruption of nucleoporins and lamin isoforms demonstrates 85 the interdependence of the spatial distributions of lamin fibers and NPCs. 86 2 of 27 Manuscript to be submitted to eLife WT WT Lmnb1 -/-Lmna -/-LA LB1 LB1 LA LB2 LC NPC NPC NPC NPC NPC NPC A B Figure 1. Co-Distribution of Lamin Fibers and NPCs. Colabeling of lamins and nuclear pore complexes in wt and lamin KO MEF nuclei using indire...

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Section: Discussionmentioning

confidence: 99%

Computational analysis of lamin isoform interactions with nuclear pore complexes

Kittisopikul

Shimi

Tatli

et al. 2020

Preprint

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“…A method relying on the fluorophore coordinate list directly has been developed to perform a similar operation to how steerable filters are processed [36]. The method proceeds by using an angular version of Ripley K’s function to determine orientation and traces fibers by assigning subsequent localizations to the fiber based on localized density and orientation [36]. The localized density is based on the area of the polygons contained within the Voronoi tessellation.…”

Section: Methodsmentioning

confidence: 99%

Quantitative Analysis of Nuclear Lamins Imaged by Super-Resolution Light Microscopy

Kittisopikul

Virtanen

Taimen

et al. 2019

Cells

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The nuclear lamina consists of a dense fibrous meshwork of nuclear lamins, Type V intermediate filaments, and is ~14 nm thick according to recent cryo-electron tomography studies. Recent advances in light microscopy have extended the resolution to a scale allowing for the fine structure of the lamina to be imaged in the context of the whole nucleus. We review quantitative approaches to analyze the imaging data of the nuclear lamina as acquired by structured illumination microscopy (SIM) and single molecule localization microscopy (SMLM), as well as the requisite cell preparation techniques. In particular, we discuss the application of steerable filters and graph-based methods to segment the structure of the four mammalian lamin isoforms (A, C, B1, and B2) and extract quantitative information.

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“…They are used either alone or in combination with the other cluster analysis methods. 53 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 We summarize how the methods are adopted for SMLM cluster analysis of the different biological applications in Table 2 .…”

Section: Main Textmentioning

confidence: 99%

A Review of Super-Resolution Single-Molecule Localization Microscopy Cluster Analysis and Quantification Methods

2020

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Single-molecule localization microscopy (SMLM) is a relatively new imaging modality, winning the 2014 Nobel Prize in Chemistry, and considered as one of the key super-resolution techniques. SMLM resolution goes beyond the diffraction limit of light microscopy and achieves resolution on the order of 10–20 nm. SMLM thus enables imaging single molecules and study of the low-level molecular interactions at the subcellular level. In contrast to standard microscopy imaging that produces 2D pixel or 3D voxel grid data, SMLM generates big data of 2D or 3D point clouds with millions of localizations and associated uncertainties. This unprecedented breakthrough in imaging helps researchers employ SMLM in many fields within biology and medicine, such as studying cancerous cells and cell-mediated immunity and accelerating drug discovery. However, SMLM data quantification and interpretation methods have yet to keep pace with the rapid advancement of SMLM imaging. Researchers have been actively exploring new computational methods for SMLM data analysis to extract biosignatures of various biological structures and functions. In this survey, we describe the state-of-the-art clustering methods adopted to analyze and quantify SMLM data and examine the capabilities and shortcomings of the surveyed methods. We classify the methods according to (1) the biological application (i.e., the imaged molecules/structures), (2) the data acquisition (such as imaging modality, dimension, resolution, and number of localizations), and (3) the analysis details (2D versus 3D, field of view versus region of interest, use of machine-learning and multi-scale analysis, biosignature extraction, etc.). We observe that the majority of methods that are based on second-order statistics are sensitive to noise and imaging artifacts, have not been applied to 3D data, do not leverage machine-learning formulations, and are not scalable for big-data analysis. Finally, we summarize state-of-the-art methodology, discuss some key open challenges, and identify future opportunities for better modeling and design of an integrated computational pipeline to address the key challenges.

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Quantitative fibre analysis of single-molecule localization microscopy data

Cited by 28 publications

References 41 publications

Computational analysis of lamin isoform interactions with nuclear pore complexes

Computational analysis of lamin isoform interactions with nuclear pore complexes

Quantitative Analysis of Nuclear Lamins Imaged by Super-Resolution Light Microscopy

A Review of Super-Resolution Single-Molecule Localization Microscopy Cluster Analysis and Quantification Methods

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