Cell Tracking Profiler – a user-driven analysis framework for evaluating 4D live-cell imaging data

Mitchell, Claire; Caroff, Lauryanne; Solís-Lemus, José Alonso; Reyes-Aldasoro, Constantino Carlos; Vigilante, Alessandra; Chaumont, Fabrice de; Dufour, Alexandre; Dallongeville, Stéphane; Olivo‐Marin, Jean‐Christophe; Knight, Robert D.

doi:10.1242/jcs.241422

Cited by 7 publications

(4 citation statements)

References 39 publications

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“…The binary probability maps and unsegmented/raw time stacks were then merged to form multi‐channel time stacks, which were then used for the cell body tracking. The image analysis software Imaris (Bitplane, Oxford Instruments) was used for cell body tracking as its high efficiency in tracking cell migration has been well reported (Mitchell et al , 2020 ). Using the “Spots” function, segmented cell bodies in the binary probability maps were recognized as spots and their movement over time was captured with the “Autoregressive Motion” algorithm, creating tracks for the migration of each cell body.…”

Section: Methodsmentioning

confidence: 99%

Neurotransmitter signaling regulates distinct phases of multimodal human interneuron migration

et al. 2021

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Inhibitory GABAergic interneurons migrate over long distances from their extracortical origin into the developing cortex. In humans, this process is uniquely slow and prolonged, and it is unclear whether guidance cues unique to humans govern the various phases of this complex developmental process. Here, we use fused cerebral organoids to identify key roles of neurotransmitter signaling pathways in guiding the migratory behavior of human cortical interneurons. We use scRNAseq to reveal expression of GABA, glutamate, glycine, and serotonin receptors along distinct maturation trajectories across interneuron migration. We develop an image analysis software package, TrackPal, to simultaneously assess 48 parameters for entire migration tracks of individual cells. By chemical screening, we show that different modes of interneuron migration depend on distinct neurotransmitter signaling pathways, linking transcriptional maturation of interneurons with their migratory behavior. Altogether, our study provides a comprehensive quantitative analysis of human interneuron migration and its functional modulation by neurotransmitter signaling.

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Section: Methodsmentioning

confidence: 99%

Neurotransmitter signaling regulates distinct phases of multimodal human interneuron migration

et al. 2021

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“…Quantitative phase imaging (Cacace, Bianco, & Ferraro, 2020;Kasprowicz, Suman, & O'Toole, 2017) offers higher contrast and the ability to segment images for extracting quantitative information. Cell growth, cell movement (Hilsenbeck et al, 2016;Aragaki, Ogoh, Kondo, & Aoki, 2022;Mitchell et al, 2020), and the behavior of cell populations (Schott et al, 2018) can be studied. Label-free imaging is advantageous because it avoids the use of fluorescent dyes that can perturb physiological function in cells, particularly if detergents are required to permeabilize membranes for successful labeling.…”

Section: Other Label-free Strategiesmentioning

confidence: 99%

Multi‐Photon Microscopy

Sanderson

2023

Current Protocols

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In this series of papers on light microscopy imaging, we have covered the fundamentals of microscopy, super-resolution microscopy, and lightsheet microscopy. This last review covers multi-photon microscopy with a brief reference to intravital imaging and Brainbow labeling. Multi-photon microscopy is often referred to as two-photon microscopy. Indeed, using two-photon microscopy is by far the most common way of imaging thick tissues; however, it is theoretically possible to use a higher number of photons, and three-photon microscopy is possible. Therefore, this review is titled "multi-photon microscopy." Another term for describing multi-photon microscopy is "non-linear" microscopy because fluorescence intensity at the focal spot depends upon the average squared intensity rather than the squared average intensity; hence, non-linear optics (NLO) is an alternative name for multi-photon microscopy. It is this non-linear relationship (or third exponential power in the case of three-photon excitation) that determines the axial optical sectioning capability of multi-photon imaging. In this paper, the necessity for two-photon or multi-photon imaging is explained, and the method of optical sectioning by multi-photon microscopy is described. Advice is also given on what fluorescent markers to use and other practical aspects of imaging thick tissues. The technique of Brainbow imaging is discussed. The review concludes with a description of intravital imaging of the mouse.

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“…Different tracking software have been published [e.g. utrack (Jaqaman et al, 2008), TrackMate (Tinevez et al, 2017), MTT (Serge et al, 2008), MSSEF-TSAKF (Jaiswal et al, 2015), maptrack (Feng et al, 2011), Cell Tracking Profiler (Mitchell et al, 2020), btrack (Ulicna et al, 2021)] to track individual biomolecules or extended objects with a shape, such as cells [e.g. TrackMate (Ershov et al, 2021) and CellProfiler (Stirling et al, 2021)], to obtain spatial information and to quantify their kinetics.…”

Section: Introductionmentioning

confidence: 99%

STracking: a free and open-source Python library for particle tracking and analysis

et al. 2022

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Summary Analysis of intra and extra cellular dynamic like vesicles transport involves particle tracking algorithms. The design of a particle tracking pipeline is a routine but tedious task. Therefore, particle dynamics analysis is often performed by combining several pieces of software (filtering, detection, tracking…) requiring many manual operations, and thus leading to poorly reproducible results. Given the new segmentation tools based on deep learning, modularity and interoperability between software have become essential in particle tracking algorithms. A good synergy between a particle detector and a tracker is of paramount importance. In addition, a user-friendly interface to control the quality of estimated trajectories is necessary. To address these issues, we developed STracking, a python library that allows combining algorithms into standardized particle tracking pipelines. Availability and Implementation STracking is available as a python library using “pip install” and the source code is publicly available on GitHub (https://github.com/sylvainprigent/stracking). A graphical interface is available using two napari plugins: napari-stracking and napari-tracks-reader. These napari plugins can be installed via the napari plugins menu or using “pip install”. The napari plugin source codes are available on GitHub (https://github.com/sylvainprigent/napari-tracks-reader, https://github.com/sylvainprigent/napari-stracking). Supplementary information Supplementary data are available at Bioinformatics online.

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Cell Tracking Profiler – a user-driven analysis framework for evaluating 4D live-cell imaging data

Cited by 7 publications

References 39 publications

Neurotransmitter signaling regulates distinct phases of multimodal human interneuron migration

Neurotransmitter signaling regulates distinct phases of multimodal human interneuron migration

Multi‐Photon Microscopy

STracking: a free and open-source Python library for particle tracking and analysis

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