Differential Mast Cell Outcomes Are Sensitive to FcεRI-Syk Binding Kinetics

Schwartz, Samantha L.; Cleyrat, Cédric; Olah, Mark J.; Relich, Peter K.; Phillips, Genevieve K; Hlavacek, William S.; Lidke, Keith A.; Wilson, Bridget S.; Lidke, Diane S.

doi:10.1101/147595

Cited by 8 publications

(10 citation statements)

References 80 publications

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“…For this work, we used a consistent tracking software so we could focus on assessing the quality of the initial spot detection algorithm chosen. The in-house tracking software used here (Relich, 2016;Schwartz et al, 2017), based on the LAP used in u-track (Jaqaman et al, 2008), in combination with Cega, allowed for tracking of moving motors within a dense background. Importantly, Cega spot detection allowed for the tracking of motors that change directions, something that programs such as u-track (Jaqaman et al, 2008) and…”

Section: Discussionmentioning

confidence: 99%

“…Cega detection was performed on simulated data with mean photon emissions of 200, or 2.8 SNR, as this SNR is similar to that of the dimmer particles within our experimental data. After candidate finding, simulated motor spot coordinates were connected into trajectories using an in-house tracking software (Relich, 2016;Schwartz et al, 2017) based on the linear assignment problem (LAP) used in u-track (Jaqaman et al, 2008). The trajectories formed after application of Cega or median background subtraction were then displayed on kymographs and compared against the known simulated spot trajectories.…”

Section: Trackingmentioning

confidence: 99%

“…The tracking software implemented for this manuscript was adapted from the MATLAB software developed for (Relich, 2016;Schwartz et al, 2017), and is based on the software used in u-track (Jaqaman et al, 2008). A few minor modifications were applied to make the workflow amenable to motor proteins, primarily the blob finding routine for initial candidate selection was replaced with Cega.…”

Section: Tracking Softwarementioning

confidence: 99%

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Cega: a single particle segmentation algorithm to identify moving particles in a noisy system

Masucci

Relich

Ostap

et al. 2021

MBoC

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Improvements to particle tracking algorithms are required to effectively analyze the motility of biological molecules in complex or noisy systems. A typical single particle tracking (SPT) algorithm detects particle coordinates for trajectory assembly. However, particle detection filters fail for datasets with low signal-to-noise levels. When tracking molecular motors in complex systems, standard techniques often fail to separate the fluorescent signatures of moving particles from background signal. We developed an approach to analyze the motility of kinesin motor proteins moving along the microtubule cytoskeleton of extracted neurons using the Kullback-Leibler (KL) divergence to identify regions where there are significant differences between models of moving particles and background signal. We tested our software on both simulated and experimental data and found a noticeable improvement in SPT capability and a higher identification rate of motors as compared to current methods. This algorithm, called Cega, for ‘find the object’, produces data amenable to conventional blob detection techniques that can then be used to obtain coordinates for downstream SPT processing. We anticipate that this algorithm will be useful for those interested in tracking moving particles in complex in vitro or in vivo environments. [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text]

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Section: Discussionmentioning

confidence: 99%

Section: Trackingmentioning

confidence: 99%

Section: Tracking Softwarementioning

confidence: 99%

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Cega: a single particle segmentation algorithm to identify moving particles in a noisy system

Masucci

Relich

Ostap

et al. 2021

MBoC

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“…Most notably is the localization algorithm used in this study. Our chosen tracker has a localization algorithm incorporating MAPPEL software (Olah, 2017/2019), which does not incorporate the estimated background information from Cega. As a result, the returned fit statistics are sub-optimal indicators for the tracking phase.…”

Section: Discussionmentioning

confidence: 99%

“…The tracking software implemented for this manuscript was adapted from the MATLAB software developed for (Schwartz et al, 2017). A few minor modifications were applied to make the workflow amenable to motor proteins, primarily the blob finding routine for initial candidate selection was replaced with Cega.…”

Section: Methodsmentioning

confidence: 99%

Cega: A Single Particle Segmentation Algorithm to Identify Moving Particles in a Noisy System

Masucci

Relich

Ostap

et al. 2020

Preprint

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Improvements to particle tracking algorithms are required to effectively analyze the motility of biological molecules in complex or noisy systems. A typical single particle tracking (SPT) algorithm detects particle coordinates for trajectory assembly. However, particle detection filters fail for datasets with low signal-to-noise levels. When tracking molecular motors in complex systems, standard techniques often fail to separate the fluorescent signatures of moving particles from background noise. We developed an approach to analyze the motility of kinesin motor proteins moving along the microtubule cytoskeleton of extracted neurons using the Kullback-Leibler (KL) divergence to identify regions where there are significant differences between models of moving particles and background signal. We tested our software on both simulated and experimental data and found a noticeable improvement in SPT capability and a higher identification rate of motors as compared to current methods. This algorithm, called Cega, for ‘find the object’, produces data amenable to conventional blob detection techniques that can then be used to obtain coordinates for downstream SPT processing. We anticipate that this algorithm will be useful for those interested in tracking moving particles in complex in vitro or in vivo environments.

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Timescale Separation of Positive and Negative Signaling Creates History-Dependent Responses to IgE Receptor Stimulation

Harmon

Chylek

et al. 2017

Sci Rep

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The high-affinity receptor for IgE expressed on the surface of mast cells and basophils interacts with antigens, via bound IgE antibody, and triggers secretion of inflammatory mediators that contribute to allergic reactions. To understand how past inputs (memory) influence future inflammatory responses in mast cells, a microfluidic device was used to precisely control exposure of cells to alternating stimulatory and non-stimulatory inputs. We determined that the response to subsequent stimulation depends on the interval of signaling quiescence. For shorter intervals of signaling quiescence, the second response is blunted relative to the first response, whereas longer intervals of quiescence induce an enhanced second response. Through an iterative process of computational modeling and experimental tests, we found that these memory-like phenomena arise from a confluence of rapid, short-lived positive signals driven by the protein tyrosine kinase Syk; slow, long-lived negative signals driven by the lipid phosphatase Ship1; and slower degradation of Ship1 co-factors. This work advances our understanding of mast cell signaling and represents a generalizable approach for investigating the dynamics of signaling systems.

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Differential Mast Cell Outcomes Are Sensitive to FcεRI-Syk Binding Kinetics

Cited by 8 publications

References 80 publications

Cega: a single particle segmentation algorithm to identify moving particles in a noisy system

Cega: a single particle segmentation algorithm to identify moving particles in a noisy system

Cega: A Single Particle Segmentation Algorithm to Identify Moving Particles in a Noisy System

Timescale Separation of Positive and Negative Signaling Creates History-Dependent Responses to IgE Receptor Stimulation

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