Automated detection of tunneling nanotubes in 3D images.

BACKGROUND: This paper presents an automated method for the identification of thin membrane tubes in 3D fluorescence images. These tubes, referred to as tunneling nanotubes (TNTs), are newly discovered intercellular structures that connect living cells through a membrane continuity. TNTs are 50-200 nm in diameter, crossing from one cell to another at their nearest distance. In microscopic images, they are seen as straight lines. It now emerges that the TNTs represent the underlying structure of a new type of cell-to-cell communication. METHODS: Our approach for the identification of TNTs is based on a combination of biological cell markers and known image processing techniques. Watershed segmentation and edge detectors are used to find cell borders, TNTs, and image artifacts. Mathematical morphology is employed at several stages of the processing chain. Two image channels are used for the calculations to improve classification of watershed regions into cells and background. One image channel displays cell borders and TNTs, the second is used for cell classification and displays the cytoplasmic compartments of the cells. The method for cell segmentation is 3D, and the TNT detection incorporates 3D information using various 2D projections. RESULTS: The TNT- and cell-detection were applied to numerous 3D stacks of images. A success rate of 67% was obtained compared with manual identification of the TNTs. The digitalized results were used to achieve statistical information of selected properties of TNTs. CONCLUSION: To further explore these structures, automated detection and quantification is desirable. Consequently, this automated recognition tool will be useful in biological studies on cell-to-cell communication where TNT quantification is essential.     

Automated Quantification and Integrative Analysis of 2D and 3D Mitochondrial Shape and Network Properties

Mitochondrial morphology and function are coupled in healthy cells, during pathological conditions and (adaptation to) endogenous and exogenous stress. In this sense mitochondrial shape can range from small globular compartments to complex filamentous networks, even within the same cell. Understanding how mitochondrial morphological changes (i.e. “mitochondrial dynamics”) are linked to cellular (patho) physiology is currently the subject of intense study and requires detailed quantitative information. During the last decade, various computational approaches have been developed for automated 2-dimensional (2D) analysis of mitochondrial morphology and number in microscopy images. Although these strategies are well suited for analysis of adhering cells with a flat morphology they are not applicable for thicker cells, which require a three-dimensional (3D) image acquisition and analysis procedure. Here we developed and validated an automated image analysis algorithm allowing simultaneous 3D quantification of mitochondrial morphology and network properties in human endothelial cells (HUVECs). Cells expressing a mitochondria-targeted green fluorescence protein (mitoGFP) were visualized by 3D confocal microscopy and mitochondrial morphology was quantified using both the established 2D method and the new 3D strategy. We demonstrate that both analyses can be used to characterize and discriminate between various mitochondrial morphologies and network properties. However, the results from 2D and 3D analysis were not equivalent when filamentous mitochondria in normal HUVECs were compared with circular/spherical mitochondria in metabolically stressed HUVECs treated with rotenone (ROT). 2D quantification suggested that metabolic stress induced mitochondrial fragmentation and loss of biomass. In contrast, 3D analysis revealed that the mitochondrial network structure was dissolved without affecting the amount and size of the organelles. Thus, our results demonstrate that 3D imaging and quantification are crucial for proper understanding of mitochondrial shape and topology in non-flat cells. In summary, we here present an integrative method for unbiased 3D quantification of mitochondrial shape and network properties in mammalian cells.     

Enhanced Adenovirus Transduction of hMSCs Using 3D Hydrogel Cell Carriers

Hydrogels are increasingly being investigated as a means to implant cells for tissue engineering. One way to further enhance the repair response would be to combine the hydrogel cell carrier with gene transfer. Gene therapy, using adenoviral vectors, is an effective way to provide transient delivery of bioactive factors. However, current protocols require further optimization, especially if they are to be transferred into the clinic. This study opted to compare the efficiency of protocols for standard two-dimensional (2D) versus three-dimensional (3D), adenoviral-mediated, transduction of human mesenchymal stem cells. Two different multiplicities of infection were tested. After encapsulation in fibrin, alginate or agarose, cells were cultured for 28 days. Transduction in 3D showed a much higher efficiency, compared to standard 2D transduction protocols. In 3D, the amount of transgene produced was significantly higher, for every condition investigated. Furthermore, transduction in 3D does not require a cell culture step and can be conducted within the operating theatre. In conclusion, it was demonstrated that 3D transduction, using adenoviral vectors, is superior to standard transduction protocols in 2D. It therefore, might help increasing its administration in tissue engineering and clinical applications.     

Visualization and correction of automated segmentation,tracking and lineaging from 5-D stem cell image sequences

Background

Neural stem cells are motile and proliferative cells that undergo mitosis, dividing to produce daughter cells and ultimately generating differentiated neurons and glia. Understanding the mechanisms controlling neural stem cell proliferation and differentiation will play a key role in the emerging fields of regenerative medicine and cancer therapeutics. Stem cell studies in vitro from 2-D image data are well established. Visualizing and analyzing large three dimensional images of intact tissue is a challenging task. It becomes more difficult as the dimensionality of the image data increases to include time and additional fluorescence channels. There is a pressing need for 5-D image analysis and visualization tools to study cellular dynamics in the intact niche and to quantify the role that environmental factors play in determining cell fate.

Results

We present an application that integrates visualization and quantitative analysis of 5-D (x,y,z,t,channel) and large montage confocal fluorescence microscopy images. The image sequences show stem cells together with blood vessels, enabling quantification of the dynamic behaviors of stem cells in relation to their vascular niche, with applications in developmental and cancer biology. Our application automatically segments, tracks, and lineages the image sequence data and then allows the user to view and edit the results of automated algorithms in a stereoscopic 3-D window while simultaneously viewing the stem cell lineage tree in a 2-D window. Using the GPU to store and render the image sequence data enables a hybrid computational approach. An inference-based approach utilizing user-provided edits to automatically correct related mistakes executes interactively on the system CPU while the GPU handles 3-D visualization tasks.

Conclusions

By exploiting commodity computer gaming hardware, we have developed an application that can be run in the laboratory to facilitate rapid iteration through biological experiments. We combine unsupervised image analysis algorithms with an interactive visualization of the results. Our validation interface allows for each data set to be corrected to 100% accuracy, ensuring that downstream data analysis is accurate and verifiable. Our tool is the first to combine all of these aspects, leveraging the synergies obtained by utilizing validation information from stereo visualization to improve the low level image processing tasks.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-328) contains supplementary material, which is available to authorized users.     

基于VTK的医学图像三维可视化系统

医学图像的三维可视化可以通过可视化工具包（VTK）提供的API实现。VTK是医学图像可视化的开法工具包,它把可视化的算法封装起来,利用简单的代码生成所需图形。基于VTK的医学图像三维可视化系统阐述了如何借助VTKAPI读入二维医学图像序列、操作二维图像、重建三维图像以及进行三维图像可视化的全套方案,为临床医生的诊断、治疗提供了有益的途径。     

An automatic 2D-3D image matching method for reproducing spatial knee joint positions using single or dual fluoroscopic images

Fluoroscopic image technique, using either a single image or dual images, has been widely applied to measure in vivo human knee joint kinematics. However, few studies have compared the advantages of using single and dual fluoroscopic images. Furthermore, due to the size limitation of the image intensifiers, it is possible that only a portion of the knee joint could be captured by the fluoroscopy during dynamic knee joint motion. In this paper, we presented a systematic evaluation of an automatic 2D-3D image matching method in reproducing spatial knee joint positions using either single or dual fluoroscopic image techniques. The data indicated that for the femur and tibia, their spatial positions could be determined with an accuracy and precision less than 0.2?mm in translation and less than 0.4° in orientation when dual fluoroscopic images were used. Using single fluoroscopic images, the method could produce satisfactory accuracy in joint positions in the imaging plane (in average up to 0.5?mm in translation and 1.3° in rotation), but large variations along the out-plane direction (in average up to 4.0?mm in translation and 2.2° in rotation). The precision of using single fluoroscopic images to determine the actual knee positions was worse than its accuracy obtained. The data also indicated that when using dual fluoroscopic image technique, if the knee joint outlines in one image were incomplete by 80%, the algorithm could still reproduce the joint positions with high precisions.     

Adaptive filtering for enhancement of the osteocyte cell network in 3D microtomography images

The osteocyte cell network in bone tissue is thought to orchestrate tissue adaptation and remodeling, thus holding responsibility for tissue quality. Previously, this structure has been studied mainly in 2D and its architecture and functions are not fully elucidated. The assessment of the osteocyte system is prerequisite for deeper understanding of bone remodeling and for advances in management of bone diseases. Our goal is to enable 3D isotropic imaging of bone at cellular level and to develop algorithms for quantitative image analysis of the cell network. We recently demonstrated accurate 3D imaging of this cell structure with synchrotron radiation tomography at submicrometric scale. Due to the limited spatial resolution of the imaging system and the constraints in terms of radiation dose, the images suffer from low signal to noise ratio and the detection of the cell dendrites is challenging. Here we detail a method for enhancement of the osteocyte network in human bone from 3D microtomography images. The approach combines Hessian-based 3D line enhancement and bilateral filtering. Our method enables extraction of the interconnected cells from noisy images, preserving the integrity of the cells and of their slender dendrites. Qualitative and quantitative results are presented.     

An Analytical Tool that Quantifies Cellular Morphology Changes from Three-dimensional Fluorescence Images

The most common software analysis tools available for measuring fluorescence images are for two-dimensional (2D) data that rely on manual settings for inclusion and exclusion of data points, and computer-aided pattern recognition to support the interpretation and findings of the analysis. It has become increasingly important to be able to measure fluorescence images constructed from three-dimensional (3D) datasets in order to be able to capture the complexity of cellular dynamics and understand the basis of cellular plasticity within biological systems. Sophisticated microscopy instruments have permitted the visualization of 3D fluorescence images through the acquisition of multispectral fluorescence images and powerful analytical software that reconstructs the images from confocal stacks that then provide a 3D representation of the collected 2D images. Advanced design-based stereology methods have progressed from the approximation and assumptions of the original model-based stereology¹ even in complex tissue sections². Despite these scientific advances in microscopy, a need remains for an automated analytic method that fully exploits the intrinsic 3D data to allow for the analysis and quantification of the complex changes in cell morphology, protein localization and receptor trafficking. Current techniques available to quantify fluorescence images include Meta-Morph (Molecular Devices, Sunnyvale, CA) and Image J (NIH) which provide manual analysis. Imaris (Andor Technology, Belfast, Northern Ireland) software provides the feature MeasurementPro, which allows the manual creation of measurement points that can be placed in a volume image or drawn on a series of 2D slices to create a 3D object. This method is useful for single-click point measurements to measure a line distance between two objects or to create a polygon that encloses a region of interest, but it is difficult to apply to complex cellular network structures. Filament Tracer (Andor) allows automatic detection of the 3D neuronal filament-like however, this module has been developed to measure defined structures such as neurons, which are comprised of dendrites, axons and spines (tree-like structure). This module has been ingeniously utilized to make morphological measurements to non-neuronal cells³, however, the output data provide information of an extended cellular network by using a software that depends on a defined cell shape rather than being an amorphous-shaped cellular model. To overcome the issue of analyzing amorphous-shaped cells and making the software more suitable to a biological application, Imaris developed Imaris Cell. This was a scientific project with the Eidgenössische Technische Hochschule, which has been developed to calculate the relationship between cells and organelles. While the software enables the detection of biological constraints, by forcing one nucleus per cell and using cell membranes to segment cells, it cannot be utilized to analyze fluorescence data that are not continuous because ideally it builds cell surface without void spaces. To our knowledge, at present no user-modifiable automated approach that provides morphometric information from 3D fluorescence images has been developed that achieves cellular spatial information of an undefined shape (Figure 1). We have developed an analytical platform using the Imaris core software module and Imaris XT interfaced to MATLAB (Mat Works, Inc.). These tools allow the 3D measurement of cells without a pre-defined shape and with inconsistent fluorescence network components. Furthermore, this method will allow researchers who have extended expertise in biological systems, but not familiarity to computer applications, to perform quantification of morphological changes in cell dynamics.     

A Novel Gaussian Extrapolation Approach for 2D Gel Electrophoresis Saturated Protein Spots

Analysis of images obtained from two-dimensional gel electrophoresis (2D-GE) is a topic of utmost importance in bioinformatics research, since commercial and academic software available currently has proven to be neither completely effective nor fully automatic, often requiring manual revision and refinement of computer generated matches. In this work, we present an effective technique for the detection and the reconstruction of over-saturated protein spots. Firstly, the algorithm reveals overexposed areas, where spots may be truncated, and plateau regions caused by smeared and overlapping spots. Next, it reconstructs the correct distribution of pixel values in these overexposed areas and plateau regions, using a two-dimensional least-squares fitting based on a generalized Gaussian distribution. Pixel correction in saturated and smeared spots allows more accurate quantification, providing more reliable image analysis results. The method is validated for processing highly exposed 2D-GE images, comparing reconstructed spots with the corresponding non-saturated image, demonstrating that the algorithm enables correct spot quantification.     

Development of a 3D optical scanner for evaluating patient-specific dose distributions

PurposeThis paper describes the hardware and software characteristics of a 3D optical scanner (P3DS) developed in-house. The P3DS consists of an LED light source, diffuse screen, step motor, CCD camera, and scanner management software with 3D reconstructed software.Materials and methodWe performed optical simulation, 2D and 3D reconstruction image testing, and pre-clinical testing for the P3DS. We developed the optical scanner with three key characteristics in mind. First, we developed a continuous scanning method to expand possible clinical applications. Second, we manufactured a collimator to improve image quality by reducing scattering from the light source. Third, we developed an optical scanner with changeable camera positioning to enable acquisition of optimal images according to the size of the gel dosimeter.ResultsWe confirmed ray-tracing in P3DS with optic simulation and found that 2D projection and 3D reconstructed images were qualitatively similar to the phantom images. For pre-clinical tests, the dose distribution and profile showed good agreement among RTP, optical CT, and external beam radiotherapy film data for the axial and coronal views. The P3DS has shown that it can scan and reconstruct for evaluation of the gel dosimeter within 1 min. We confirmed that the P3DS system is a useful tool for the measurement of 3D dose distributions for 3D radiation therapy QA. Further experiments are needed to investigate quantitative analysis for 3D dose distribution.     

Highly efficient reprogramming to pluripotency and directed differentiation of human cells with synthetic modified mRNA

Clinical application of induced pluripotent stem cells (iPSCs) is limited by the low efficiency of iPSC derivation and the fact that most protocols modify the genome to effect cellular reprogramming. Moreover, safe and effective means of directing the fate of patient-specific iPSCs toward clinically useful cell types are lacking. Here we describe a simple, nonintegrating strategy for reprogramming cell fate based on administration of synthetic mRNA modified to overcome innate antiviral responses. We show that this approach can reprogram multiple human cell types to pluripotency with efficiencies that greatly surpass established protocols. We further show that the same technology can be used to efficiently direct the differentiation of RNA-induced pluripotent stem cells (RiPSCs) into terminally differentiated myogenic cells. This technology represents a safe, efficient strategy for somatic cell reprogramming and directing cell fate that has broad applicability for basic research, disease modeling, and regenerative medicine.     

Localizing Protein in 3D Neural Stem Cell Culture: a Hybrid Visualization Methodology

The importance of 3-dimensional (3D) topography in influencing neural stem and progenitor cell (NPC) phenotype is widely acknowledged yet challenging to study. When dissociated from embryonic or post-natal brain, single NPCs will proliferate in suspension to form neurospheres. Daughter cells within these cultures spontaneously adopt distinct developmental lineages (neurons, oligodendrocytes, and astrocytes) over the course of expansion despite being exposed to the same extracellular milieu. This progression recapitulates many of the stages observed over the course of neurogenesis and gliogenesis in post-natal brain and is often used to study basic NPC biology within a controlled environment. Assessing the full impact of 3D topography and cellular positioning within these cultures on NPC fate is, however, difficult. To localize target proteins and identify NPC lineages by immunocytochemistry, free-floating neurospheres must be plated on a substrate or serially sectioned. This processing is required to ensure equivalent cell permeabilization and antibody access throughout the sphere. As a result, 2D epifluorescent images of cryosections or confocal reconstructions of 3D Z-stacks can only provide spatial information about cell position within discrete physical or digital 3D slices and do not visualize cellular position in the intact sphere. Here, to reiterate the topography of the neurosphere culture and permit spatial analysis of protein expression throughout the entire culture, we present a protocol for isolation, expansion, and serial sectioning of post-natal hippocampal neurospheres suitable for epifluorescent or confocal immunodetection of target proteins. Connexin29 (Cx29) is analyzed as an example. Next, using a hybrid of graphic editing and 3D modelling softwares rigorously applied to maintain biological detail, we describe how to re-assemble the 3D structural positioning of these images and digitally map labelled cells within the complete neurosphere. This methodology enables visualization and analysis of the cellular position of target proteins and cells throughout the entire 3D culture topography and will facilitate a more detailed analysis of the spatial relationships between cells over the course of neurogenesis and gliogenesis in vitro.Both Imbeault and Valenzuela contributed equally and should be considered joint first authors.     

Image-based RSA: Roentgen stereophotogrammetric analysis based on 2D-3D image registration

Image-based Roentgen stereophotogrammetric analysis (IBRSA) integrates 2D-3D image registration and conventional RSA. Instead of radiopaque RSA bone markers, IBRSA uses 3D CT data, from which digitally reconstructed radiographs (DRRs) are generated. Using 2D-3D image registration, the 3D pose of the CT is iteratively adjusted such that the generated DRRs resemble the 2D RSA images as closely as possible, according to an image matching metric. Effectively, by registering all 2D follow-up moments to the same 3D CT, the CT volume functions as common ground. In two experiments, using RSA and using a micromanipulator as gold standard, IBRSA has been validated on cadaveric and sawbone scapula radiographs, and good matching results have been achieved. The accuracy was: |mu |< 0.083 mm for translations and |mu| < 0.023 degrees for rotations. The precision sigma in x-, y-, and z-direction was 0.090, 0.077, and 0.220 mm for translations and 0.155 degrees , 0.243 degrees , and 0.074 degrees for rotations. Our results show that the accuracy and precision of in vitro IBRSA, performed under ideal laboratory conditions, are lower than in vitro standard RSA but higher than in vivo standard RSA. Because IBRSA does not require radiopaque markers, it adds functionality to the RSA method by opening new directions and possibilities for research, such as dynamic analyses using fluoroscopy on subjects without markers and computer navigation applications.     

Fast multi-core based multimodal registration of 2D cross-sections and 3D datasets

Background  

Solving bioinformatics tasks often requires extensive computational power. Recent trends in processor architecture combine multiple cores into a single chip to improve overall performance. The Cell Broadband Engine (CBE), a heterogeneous multi-core processor, provides power-efficient and cost-effective high-performance computing. One application area is image analysis and visualisation, in particular registration of 2D cross-sections into 3D image datasets. Such techniques can be used to put different image modalities into spatial correspondence, for example, 2D images of histological cuts into morphological 3D frameworks.     

人肺巨细胞癌蛋白质组的二维电泳和计算机图象分析

为优化用于蛋白质组研究的二维电泳技术和计算机图象分析技术 ,以及初步分析比较与肿瘤细胞转移相关的蛋白质 ,以人肺巨细胞癌 (PLA- 80 1 - D、C)高、低转移株作为研究对象 ,应用 IPG-phor进行第一向等电聚焦 ,随后 ,在 Protein IPG conversion Kit上进行垂直 SDS- PAGE的分离 .利用光密度仪对银染的凝胶扫描 ,通过 PDQuest软件进行蛋白斑点检测和配比 .结果表明 :(1 )应用 IPGphor,采用样品直接加入重泡胀溶液的形式 ,增大了溶解性 ,缩短聚焦时间、增大样品负荷量 (分析型 ) ,提高了分辨率 .(2 )比较宽 (p H=3～ 1 0 L)、窄 (p H=4～ 7L)范围 IPG胶条 ,窄 p H范围的 IPG胶条具有较高的分辨率 .(3)比较 PLA- 80 1 - C、D细胞蛋白图谱之间的差异 ,其相关系数为 0 .7339± 0 .0 2 91 ;仅在 PLA- 80 1 - C株出现的蛋白为 1 79个 .     

Technical Note: Guidelines for the digital computation of 2D and 3D enamel thickness in hominoid teeth

The study of enamel thickness has received considerable attention in regard to the taxonomic, phylogenetic and dietary assessment of human and non‐human primates. Recent developments based on two‐dimensional (2D) and three‐dimensional (3D) digital techniques have facilitated accurate analyses, preserving the original object from invasive procedures. Various digital protocols have been proposed. These include several procedures based on manual handling of the virtual models and technical shortcomings, which prevent other scholars from confidently reproducing the entire digital protocol. There is a compelling need for standard, reproducible, and well‐tailored protocols for the digital analysis of 2D and 3D dental enamel thickness. In this contribution we provide essential guidelines for the digital computation of 2D and 3D enamel thickness in hominoid molars, premolars, canines and incisors. We modify previous techniques suggested for 2D analysis and we develop a new approach for 3D analysis that can also be applied to premolars and anterior teeth. For each tooth class, the cervical line should be considered as the fundamental morphological feature both to isolate the crown from the root (for 3D analysis) and to define the direction of the cross‐sections (for 2D analysis). Am J Phys Anthropol 153:305–313, 2014. © 2013 Wiley Periodicals, Inc.     

3D Tree Dimensionality Assessment Using Photogrammetry and Small Unmanned Aerial Vehicles

Detailed, precise, three-dimensional (3D) representations of individual trees are a prerequisite for an accurate assessment of tree competition, growth, and morphological plasticity. Until recently, our ability to measure the dimensionality, spatial arrangement, shape of trees, and shape of tree components with precision has been constrained by technological and logistical limitations and cost. Traditional methods of forest biometrics provide only partial measurements and are labor intensive. Active remote technologies such as LiDAR operated from airborne platforms provide only partial crown reconstructions. The use of terrestrial LiDAR is laborious, has portability limitations and high cost. In this work we capitalized on recent improvements in the capabilities and availability of small unmanned aerial vehicles (UAVs), light and inexpensive cameras, and developed an affordable method for obtaining precise and comprehensive 3D models of trees and small groups of trees. The method employs slow-moving UAVs that acquire images along predefined trajectories near and around targeted trees, and computer vision-based approaches that process the images to obtain detailed tree reconstructions. After we confirmed the potential of the methodology via simulation we evaluated several UAV platforms, strategies for image acquisition, and image processing algorithms. We present an original, step-by-step workflow which utilizes open source programs and original software. We anticipate that future development and applications of our method will improve our understanding of forest self-organization emerging from the competition among trees, and will lead to a refined generation of individual-tree-based forest models.     

Understanding 2D projections on mirrors and on windows

Representational art tries to capture a 3D world on a 2D surface, and artists often discuss this in relation to the projected image on window panes and mirrors. But are 2D projections on transparent surfaces useful to learn about projections in general? Most people are unaware of the 2D projected size of objects on the surface of mirrors. They also incorrectly expect that these projections always get smaller with distance of the target object from the mirror, and do not change with distance of the observer (when the target is stationary). In this paper we extend this result about surfaces of mirrors to surfaces of windows, and we confirm that the errors that people make are not specific to Western culture by replicating the study in China. In contrast to their errors about projections, people are more accurate at predicting how field of view will vary depending on distance of the observer from a mirror or window. To explain how this pattern of (false) beliefs can stem from experience we argue that people do not perceive projections on transparent surfaces.     

Biological activity of 1 alpha, 25-dihydroxyvitamin D derivatives--24-epi-1 alpha, 25-dihydroxyvitamin D-2 and 1 alpha,25-dihydroxyvitamin D-7

Biological activity of 24-epi-1 alpha,25-dihydroxyvitamin D-2 (24-epi-1,25(OH)2D2) and 1 alpha,25-dihydroxyvitamin D-7 (1,25(OH)2D7), the 22,23-dihydro derivative of the former compound, was investigated. Both of the vitamin D derivatives stimulated intestinal calcium transport and calcium mobilization from bones in rats; however, the effect was about 50% of that of 1 alpha,25-dihydroxyvitamin D-3 (1,25(OH)2D3). On the other hand, 24-epi-1,25(OH)2D2 and 1,25(OH)2D7 inducement of HL-60 human leukemia cell differentiation was comparable to that of 1,25(OH)2D3. Accordingly, the differentiation-inducing activity of 24-epi-1,25(OH)2D2 and 1,25(OH)2D7 was much greater than their ability to stimulate calcium metabolism. In contrast to 1,25(OH)2D3, 24-epi-1,25(OH)2D2 and 1,25(OH)2D7 exerted little hypercalcemic activity in mice. These results suggest that both vitamin D derivatives will be useful as anti-tumor agents.     

FOCAL3D: A 3-dimensional clustering package for single-molecule localization microscopy

Single-molecule localization microscopy (SMLM) is a powerful tool for studying intracellular structure and macromolecular organization at the nanoscale. The increasingly massive pointillistic data sets generated by SMLM require the development of new and highly efficient quantification tools. Here we present FOCAL3D, an accurate, flexible and exceedingly fast (scaling linearly with the number of localizations) density-based algorithm for quantifying spatial clustering in large 3D SMLM data sets. Unlike DBSCAN, which is perhaps the most commonly employed density-based clustering algorithm, an optimum set of parameters for FOCAL3D may be objectively determined. We initially validate the performance of FOCAL3D on simulated datasets at varying noise levels and for a range of cluster sizes. These simulated datasets are used to illustrate the parametric insensitivity of the algorithm, in contrast to DBSCAN, and clustering metrics such as the F1 and Silhouette score indicate that FOCAL3D is highly accurate, even in the presence of significant background noise and mixed populations of variable sized clusters, once optimized. We then apply FOCAL3D to 3D astigmatic dSTORM images of the nuclear pore complex (NPC) in human osteosaracoma cells, illustrating both the validity of the parameter optimization and the ability of the algorithm to accurately cluster complex, heterogeneous 3D clusters in a biological dataset. FOCAL3D is provided as an open source software package written in Python.