ClearVolume: open-source live 3D visualization for light-sheet microscopy

Royer, Loïc; Weigert, Martin; Günther, Ulrik; Maghelli, Nicola; Jug, Florian; Sbalzarini, Ivo F.; Myers, Eugene W.

doi:10.1038/nmeth.3372

Cited by 153 publications

(111 citation statements)

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“…HIV budding profiles were manually placed using TrakEM2 (Schindelin et al, 2012), then their co-ordinates were extracted using a python script [courtesy of Albert Cardona, Howard Hughes Medical Institute (HHMI) at the Janelia Research Campus, VA] and imported into Amira for visualisation. For 3D rendering of the entotic cell dataset, we used the ClearVolume plugin in Fiji () (Royer et al, 2015). The SBF-SEM image was inverted and the Pt signal subtracted to aid visualisation in 3D.…”

Section: Methodsmentioning

confidence: 99%

3D correlative light and electron microscopy of cultured cells using serial blockface scanning electron microscopy

Russell

Lerner

Burden

et al. 2016

Journal of Cell Science

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The processes of life take place in multiple dimensions, but imaging these processes in even three dimensions is challenging. Here, we describe a workflow for 3D correlative light and electron microscopy (CLEM) of cell monolayers using fluorescence microscopy to identify and follow biological events, combined with serial blockface scanning electron microscopy to analyse the underlying ultrastructure. The workflow encompasses all steps from cell culture to sample processing, imaging strategy, and 3D image processing and analysis. We demonstrate successful application of the workflow to three studies, each aiming to better understand complex and dynamic biological processes, including bacterial and viral infections of cultured cells and formation of entotic cell-in-cell structures commonly observed in tumours. Our workflow revealed new insight into the replicative niche of Mycobacterium tuberculosis in primary human lymphatic endothelial cells, HIV-1 in human monocyte-derived macrophages, and the composition of the entotic vacuole. The broad application of this 3D CLEM technique will make it a useful addition to the correlative imaging toolbox for biomedical research.

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Section: Methodsmentioning

confidence: 99%

3D correlative light and electron microscopy of cultured cells using serial blockface scanning electron microscopy

Russell

Lerner

Burden

et al. 2016

Journal of Cell Science

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“…Furthermore, collaborative efforts between KNIP, ImageJ2 (University of Wisconsin) and Fiji (MPI-CBG Dresden) drive the continuous development of these libraries. Based on this technical infrastructure, several other well-known open source projects, for example ImageJ2 and FIJI themselves, Ilastik (Sommer et al, 2011), CellProfiler (Carpenter et al, 2006), ClearVolume (Royer et al, 2015) and OMERO (Allan et al, 2012) have been integrated in KNIP. Most notably, integrating with ImageJ2 and FIJI allows scientists to easily turn ImageJ2 plugins into KNIME nodes, without having to be able to script or program a single line of code.…”

Section: Image Processingmentioning

confidence: 99%

KNIME for reproducible cross-domain analysis of life science data

Fillbrunn

Dietz

Pfeuffer

et al. 2017

Journal of Biotechnology

172

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Experiments in the life sciences often involve tools from a variety of domains such as mass spectrometry, next generation sequencing, or image processing. Passing the data between those tools often involves complex scripts for controlling data flow, data transformation, and statistical analysis. Such scripts are not only prone to be platform dependent, they also tend to grow as the experiment progresses and are seldomly well documented, a fact that hinders the reproducibility of the experiment. Workflow systems such as KNIME Analytics Platform aim to solve these problems by providing a platform for connecting tools graphically and guaranteeing the same results on different operating systems. As an open source software, KNIME allows scientists and programmers to provide their own extensions to the scientific community. In this review paper we present selected extensions from the life sciences that simplify data exploration, analysis, and visualization and are interoperable due to KNIME's unified data model. Additionally, we name other workflow systems that are commonly used in the life sciences and highlight their similarities and differences to KNIME.

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“…Scalable usability of any new optical technique requires widespread dissemination of hardware and software for data acquisition and of software for data analysis. Commercial software and, more recently, open-source software packages have begun to meet many needs for visualization and analysis of terabyte-sized imaging volumes 2–4 . As the options for data analysis continue to expand and evolve, it becomes increasingly difficult to make ideal use of the full capabilities of widefield, confocal, and multiphoton microscopes already present in many labs because of a lack of automated and customizable acquisition software 5 .…”

Section: To the Editormentioning

confidence: 99%