Lineage mapper: A versatile cell and particle tracker

Chalfoun, Joe; Majurski, Michael; Dima, Alden A.; Halter, Michael; Bhadriraju, Kiran; Brady, Mary

doi:10.1038/srep36984

Cited by 44 publications

(30 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…B. Individual cells are tracked between successive images in a time lapse series [36] . Intracellular fluorescence intensity is computed using the segmentation mask from the transmitted light image applied to the corresponding fluorescence image.…”

Section: Advances In Imaging Technologies For Probing Multidimensionamentioning

confidence: 99%

“…These general-purpose tools facilitate the building of quantitative workflows for the analysis of live cell imaging data. Software tools designed specifically for the analysis time lapse image data include LEVER [42] , Lineage Mapper [36] , Sequitr [43] and The Tracking Tool, tTt [38] . Together, these tools are facilitating the quantitation of cellular dynamics by time lapse microscopy.…”

Section: Advances In Imaging Technologies For Probing Multidimensionamentioning

confidence: 99%

See 1 more Smart Citation

Probing pluripotency gene regulatory networks with quantitative live cell imaging

Plant

Halter

Stinson

2020

Computational and Structural Biotechnology Journal

Self Cite

View full text Add to dashboard Cite

Live cell imaging uniquely enables the measurement of dynamic events in single cells, but it has not been used often in the study of gene regulatory networks. Network components can be examined in relation to one another by quantitative live cell imaging of fluorescent protein reporter cell lines that simultaneously report on more than one network component. A series of dual-reporter cell lines would allow different combinations of network components to be examined in individual cells. Dynamical information about interacting network components in individual cells is critical to predictive modeling of gene regulatory networks, and such information is not accessible through omics and other end point techniques. Achieving this requires that gene-edited cell lines are appropriately designed and adequately characterized to assure the validity of the biological conclusions derived from the expression of the reporters. In this brief review we discuss what is known about the importance of dynamics to network modeling and review some recent advances in optical microscopy methods and image analysis approaches that are making the use of quantitative live cell imaging for network analysis possible. We also discuss how strategies for genetic engineering of reporter cell lines can influence the biological relevance of the data.

show abstract

Section: Advances In Imaging Technologies For Probing Multidimensionamentioning

confidence: 99%

Section: Advances In Imaging Technologies For Probing Multidimensionamentioning

confidence: 99%

Probing pluripotency gene regulatory networks with quantitative live cell imaging

Plant

Halter

Stinson

2020

Computational and Structural Biotechnology Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…Even though open-source cell tracking software, such as Lineage Mapper is available [42], segmented data may not be directly compatible with Lineage Mapper if single cells are not segmented into individual instances correctly in every frame. Because Lineage Mapper is fully automatic, a manual verification process is not available in Lineage Mapper.…”

Section: Validation Of Usiigacimentioning

confidence: 99%

Usiigaci: Instance-aware cell tracking in stain-free phase contrast microscopy enabled by machine learning

Tsai

Gajda

Sloan³

et al. 2019

Preprint

View full text Add to dashboard Cite

Stain-free, single-cell segmentation and tracking is tantamount to the holy grail of microscopic cell migration analysis. Phase contrast microscopy (PCM) images with cells at high density are notoriously difficult to segment accurately; thus, manual segmentation remains the de facto standard practice. In this work, we introduce Usiigaci, an all-in-one, semi-automated pipeline to segment, track, and visualize cell movement and morphological changes in PCM. Stain-free, instance-aware segmentation is accomplished using a mask regional convolutional neural network (Mask R-CNN). A Trackpy-based cell tracker with a graphical user interface is developed for cell tracking and data verification. The performance of Usiigaci is validated with electrotaxis of NIH/3T3 fibroblasts. Usiigaci provides highly accurate cell movement and morphological information for quantitative cell migration analysis.Cell migration is a fundamental cell behavior that underlies various phys-2 iological processes, including development, tissue maintenance, immunity, 3 and tissue regeneration, as well as pathological processes such as metastasis. 4Many in vitro as well as in vivo platforms have been developed to investigate 5 molecular mechanisms underlying cell migration in different microenviron-6 ments with the aid of microscopy. To analyze single-or collective-cell migra-7 tion, reliable segmentation of each individual cell in microscopic images is 8 necessary in order to extract location as well as morphological information. 9Among bright-field microscopy techniques, Zernike's phase contrast mi-10 croscopy (PCM) is favored by biologists for its ability to translate phase dif-11 ferences from cellular components into amplitude differences, so as to make 12 the cell membrane, the nucleus, and vacuoles more visible [1]. However, 13PCM images are notoriously difficult to segment correctly using conven-14 tional computer vision methods, due to the low contrast between cells and 15 their background [2]. For this reason, many cell migration experiments still 16 rely on fluorescent labeling of cells or manual tracking. Fluorescent labeling 17 of cells requires transgenic expression of fluorescent proteins or cells tagged 18 with fluorescent compounds, both of which can be toxic to cells and which 19 require extensive validation of phenotypic changes. Although thresholding 20 fluorescent images is relatively straightforward, cells that are in close prox-21 imity are often indistinguishable in threshold results. On the other hand, 22 manual tracking of cell migration is labor-intensive and prone to operator 23 error. Conducting high-throughput microscopy experiments is already possi-24 ble thanks to methodology and instrumental advances, but current analytical 25 techniques to interpret results quantitatively face major obstacles due to im-26 perfect cell segmentation and tracking [3]. Moreover, cell movement is not 27 the only parameter of interest in cell migration. For cell migration guided 28 by environmental gradients, shear stress, surface topolog...

show abstract

“…In this context, lineage mapping refers to the tracking of both cell position and cell division into progeny within the developmental stage of a living organism. While canonical cell lineage mapping has been determined for simpler organisms such as Caenorhabiditis elegans , modern lineage mapping is nevertheless an active field of research for more complex organisms as well as for the analysis of mutant phenotypes and other more in‐depth investigations (Amat et al., ; Chalfoun et al., ; Christensen et al., ; Heid, Voss, & Soll, ; McDole et al., ; Sato, Rancourt, Sato, & Satoh, ; Stegmaier et al., ; Tassy, Daian, Hudson, Bertrand, & Lemaire, ; Winter et al., ).…”

Section: Analysis and Modeling Of Tracking Datamentioning

confidence: 99%

Practical Considerations in Particle and Object Tracking and Analysis

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

Lineage mapper: A versatile cell and particle tracker

Cited by 44 publications

References 31 publications

Probing pluripotency gene regulatory networks with quantitative live cell imaging

Probing pluripotency gene regulatory networks with quantitative live cell imaging

Usiigaci: Instance-aware cell tracking in stain-free phase contrast microscopy enabled by machine learning

Practical Considerations in Particle and Object Tracking and Analysis

Contact Info

Product

Resources

About