LEVER: software tools for segmentation, tracking and lineaging of proliferating cells

Winter, Mark; Mankowski, Walter; Wait, Eric; Temple, Sally; Cohen, Andrew R.

doi:10.1093/bioinformatics/btw406

Cited by 47 publications

(47 citation statements)

References 11 publications

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“…The same pixel-based approach was applied to both 2-D confocal and 3-D LLS multi-channel time-lapse images. A software tool called LEVER 3-D 28 originally developed for characterizing neural stem cell interaction with blood vessels 15 was used here to visualize the interaction between mitochondria and the other organelles (Fig. 4d–e), or the interaction between ERMCSs and other organelles (Fig.…”

Section: Methodsmentioning

confidence: 99%

Applying systems-level spectral imaging and analysis to reveal the organelle interactome

Valm¹,

Cohen²,

Legant³

et al. 2017

Nature

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881

792

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The organization of the eukaryotic cell into discrete membrane-bound organelles allows for the separation of incompatible biochemical processes, yet the activities of these organelles must be coordinated. For example, lipid metabolism is distributed between the endoplasmic reticulum (ER) for lipid synthesis, lipid droplets (LDs) for storage and transport, mitochondria and peroxisomes for β-oxidation, and lysosomes for lipid hydrolysis and recycling1–5. Organelle contacts are increasingly understood to be vital for diverse cellular functions5–8. However, the spatial and temporal organization of organelles within the cell remains poorly characterized due to the inability of fluorescence imaging-based approaches to distinguish more than a few fluorescent labels in a single image9. Here we present a systems-level analysis of the organelle interactome using a multispectral image acquisition method that overcomes the challenge of spectral overlap in the fluorescent protein palette. We employed confocal and lattice light sheet (LLS)10 instrumentation and an imaging informatics pipeline of five steps to achieve mapping of organelle numbers/volumes/speeds/positions and dynamic inter-organelle contacts in live fibroblast cells. We describe the frequency and locality of two-, three-, four-, and five-way interactions among six different membrane-bound organelles (ER, Golgi, lysosome, peroxisome, mitochondria and LD) and show how these relationships change over time. We demonstrate that each organelle has a characteristic distribution and dispersion pattern in three-dimensional space and that there is a reproducible pattern of contacts among the six organelles, impacted by microtubule and cell nutrient status. These live-cell confocal and LLS spectral-imaging approaches are applicable to any cell system expressing multiple fluorescent probes, whether in normal conditions or when cells are exposed to disturbances such as drugs, pathogens or stress. This methodology thus offers a powerful new descriptive tool and source for hypotheses about cellular organization and dynamics.

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Section: Methodsmentioning

confidence: 99%

Applying systems-level spectral imaging and analysis to reveal the organelle interactome

Valm¹,

Cohen²,

Legant³

et al. 2017

Nature

Self Cite

881

792

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show abstract

“…Ultimately, accurate object segmentation is vital. Errors in the shape or number of targets will ultimately corrupt the results needed in the following stages of tracking (Winter, Mankowski, Wait, Temple, & Cohen, 2016)…”

Section: Of 34mentioning

confidence: 99%

Practical Considerations in Particle and Object Tracking and Analysis

et al. 2019

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The rapid advancement of live‐cell imaging technologies has enabled biologists to generate high‐dimensional data to follow biological movement at the microscopic level. Yet, the “perceived” ease of use of modern microscopes has led to challenges whereby sub‐optimal data are commonly generated that cannot support quantitative tracking and analysis as a result of various ill‐advised decisions made during image acquisition. Even optimally acquired images often require further optimization through digital processing before they can be analyzed. In writing this article, we presume our target audience to be biologists with a foundational understanding of digital image acquisition and processing, who are seeking to understand the essential steps for particle/object tracking experiments. It is with this targeted readership in mind that we review the basic principles of image‐processing techniques as well as analysis strategies commonly used for tracking experiments. We conclude this technical survey with a discussion of how movement behavior can be mathematically modeled and described. © 2019 by John Wiley & Sons, Inc.

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“…Each image is first segmented, and then tracked using the approach previously described for the LEVER (lineage editing and validation) software tools [54][55][56][57] . The segmentation processed the two FUCCI channels using a denoising algorithm 58 that models imaging noise not peer-reviewed) is the author/funder.…”

Section: Computational Image Analysismentioning

confidence: 99%

Directed, but not random, breast cancer cell migration is faster in the G1 phase of the cell cycle in 2D and 3D environments

et al. 2018

Preprint

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Cancer cell migration is essential for the early steps of metastasis, during which cancer cells move through the primary tumor and reach the blood vessels. In vivo, cancer cells are exposed to directional guidance cues, either soluble, such as gradients of growth factors, or insoluble, such as collagen fiber alignment. Depending on the number and strength of such cues, cells will migrate in a random or directed manner. Interestingly, similar cues also stimulate cell proliferation. In this regard, it is not clear whether cell cycle progression affects migration of cancer cells and whether this effect is different in random versus directed migration. In this study, we tested the effect of cell cycle progression on random and directed migration, both in 2D and 3D environments, in the breast carcinoma cell line, FUCCI-MDA-MB-231, using computational image analysis by LEVER. Directed migration in 2D was modeled as chemotaxis along a gradient of soluble EGF inside 10 µm-wide microchannels. In 3D, directed migration was modeled as contact guidance (alignotaxis) along aligned collagen fibers. Time-lapse recordings of cells in 2D and 3D revealed that directed, but not random migration, is cell cycle-dependent. In both 2D and 3D directed migration, cells in the G1 phase of the cell cycle outperformed cells in the G2 phase in terms of migration persistence and instantaneous velocity. These data suggest that in the presence of guidance cues in vivo, breast carcinoma cells in the G1 phase of the cell cycle may be more efficient in reaching vasculature.not peer-reviewed)

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LEVER: software tools for segmentation, tracking and lineaging of proliferating cells

Abstract: LEVER is available free and open source, licensed under the GNU GPLv3. Details on obtaining and using LEVER are available at http://n2t.net/ark:/87918/d9rp4t CONTACT: acohen@coe.drexel.edu.

Cited by 47 publications

References 11 publications

Applying systems-level spectral imaging and analysis to reveal the organelle interactome

Applying systems-level spectral imaging and analysis to reveal the organelle interactome

Practical Considerations in Particle and Object Tracking and Analysis

Directed, but not random, breast cancer cell migration is faster in the G1 phase of the cell cycle in 2D and 3D environments

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