TrackPad: Software for semi-automated single-cell tracking and lineage annotation

Cornwell, James; Li, J.; Mahadevan, Swarna; Draper, Jonathan S.; Joun, George; Zoellner, Hans; Asli, Naisana S.; Harvey, Richard P.; Nordon, Robert E.

doi:10.1016/j.softx.2020.100440

Cited by 14 publications

(26 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Single-cell tracking software was adapted from that previously developed in MATLAB (TrackPad: https://github.com/Jamcor/TrackPad), and was used to follow the fate of individual cells and their progeny in phase contrast time-lapse recordings [38,39]. Cells present at the beginning of experiments, were defined as the 'starting population', while sequential cell divisions generated 'first', 'second', 'third' and very occasionally 'fourth' generations of progeny.…”

Section: Single-cell Tracking and Analysismentioning

confidence: 99%

“…Moving cell location was determined during single-cell tracking in Trackpad, while further analysis was in RStudio, similar to earlier reports [38,39]. In brief, mean cell migration velocity was calculated for all tracked cells, by firstly identifying cell centroids in phase contrast images at relevant time points at 2 h intervals, performed in Trackpad.…”

Section: Determination Of Cell Migration Velocitymentioning

confidence: 99%

“…We now address these limitations by single-cell tracking of SAOS-2 co-cultured with HDF. This method records the behavior and fate of individual cells and their progeny across multiple cell divisions and generations [38][39][40]. By identifying the fate of individual cells, single-cell tracking overcomes the limitations of pooled cell assays that average the outcomes for thousands of cells.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cell-Projection Pumping of Fibroblast Contents into Osteosarcoma SAOS-2 Cells Correlates with Increased SAOS-2 Proliferation and Migration, and also with Altered Morphology

Mahadevan

Cornwell

Chami

et al. 2021

Preprint

View full text Add to dashboard Cite

We earlier reported that cell-projection pumping transfers fibroblast contents to cancer cells, and this alters cancer cell phenotype. We now report on single-cell tracking of time lapse recordings from co-cultured fluorescent fibroblasts and SAOS-2 osteosarcoma cells, tracking 5,201 cells across 7 experiments. The fluorescent lipophilic marker DiD was used to label fibroblast organelles, and to trace transfer of fibroblast cytoplasm into SAOS-2. We related SAOS-2 phenotypic change to levels of fluorescence transfer from fibroblasts to SAOS-2, and also to what we term ‘compensated fluorescence’, that numerically projects mother cell fluorescence post-mitosis, into daughter cells. Comparison of absolute with compensated fluorescence, allowed deduction if phenotypic effects in mother SAOS-2, were inherited by their daughters. SAOS-2 receipt of fibroblast fluorescence correlated by Kendall’s tau: with cell-profile area, and without evidence for persistence in daughter cells (median tau = 0.51, p < 0. 016); negatively and weakly with cell circularity, and with evidence for persistence (median tau = −0.19, p < 0.05); and very weakly with cell migration velocity, and without evidence for persistence (median tau = 0.01, p < 0.016). Also, mitotic SAOS-2 had higher rates of prior fluorescence uptake (median = 64.9 units/day), compared with non dividing cells (median = 35.6 units/day, p < 0.016), and there was no evidence for persistence post-mitosis. We conclude there is appreciable impact of cell-projection pumping on cancer cell phenotype, relevant to cancer histopathological diagnosis, clinical spread, and growth, with most effects ‘reset’ by cancer cell mitosis.

show abstract

Section: Single-cell Tracking and Analysismentioning

confidence: 99%

Section: Determination Of Cell Migration Velocitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cell-Projection Pumping of Fibroblast Contents into Osteosarcoma SAOS-2 Cells Correlates with Increased SAOS-2 Proliferation and Migration, and also with Altered Morphology

Mahadevan

Cornwell

Chami

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Uncoordinated cellular migration also reduces the similarity of intensity patterns between successive recordings, which degrades the accuracy of PIV. However, if the cells are sufficiently different from each other such that they are unambiguously detected by cross-correlation, a PIV analysis matching the size of the cells can be used to effectively track the movement of independently migrating cell [ 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

QuickPIV: Efficient 3D particle image velocimetry software applied to quantifying cellular migration during embryogenesis

Pereyra

Drusko

Krämer³

et al. 2021

BMC Bioinformatics

View full text Add to dashboard Cite

Background The technical development of imaging techniques in life sciences has enabled the three-dimensional recording of living samples at increasing temporal resolutions. Dynamic 3D data sets of developing organisms allow for time-resolved quantitative analyses of morphogenetic changes in three dimensions, but require efficient and automatable analysis pipelines to tackle the resulting Terabytes of image data. Particle image velocimetry (PIV) is a robust and segmentation-free technique that is suitable for quantifying collective cellular migration on data sets with different labeling schemes. This paper presents the implementation of an efficient 3D PIV package using the Julia programming language—quickPIV. Our software is focused on optimizing CPU performance and ensuring the robustness of the PIV analyses on biological data. Results QuickPIV is three times faster than the Python implementation hosted in openPIV, both in 2D and 3D. Our software is also faster than the fastest 2D PIV package in openPIV, written in C++. The accuracy evaluation of our software on synthetic data agrees with the expected accuracies described in the literature. Additionally, by applying quickPIV to three data sets of the embryogenesis of Tribolium castaneum, we obtained vector fields that recapitulate the migration movements of gastrulation, both in nuclear and actin-labeled embryos. We show normalized squared error cross-correlation to be especially accurate in detecting translations in non-segmentable biological image data. Conclusions The presented software addresses the need for a fast and open-source 3D PIV package in biological research. Currently, quickPIV offers efficient 2D and 3D PIV analyses featuring zero-normalized and normalized squared error cross-correlations, sub-pixel/voxel approximation, and multi-pass. Post-processing options include filtering and averaging of the resulting vector fields, extraction of velocity, divergence and collectiveness maps, simulation of pseudo-trajectories, and unit conversion. In addition, our software includes functions to visualize the 3D vector fields in Paraview.

show abstract

“…Thus, a scenario of low temporal resolution and/or high cell speed (or large movement) is a more practical and challenging problem since this setting complicates the identification of the correspondence between cells. Since existing tracking methods with the low temporal resolution are not robust to cell movements, it often requires significant manual interventions or semi-automated solution [5].…”

Section: Introductionmentioning

confidence: 99%

Single cell tracking based on Voronoi partition via stable matching

Chang

Linsley

Lamstein

et al. 2020

Preprint

View full text Add to dashboard Cite

Live-cell imaging is an important technique to study cell migration and proliferation as well as image-based profiling of drug perturbations over time. To gain biological insights from live-cell imaging data, it is necessary to identify individual cells, follow them over time and extract quantitative information. However, since often biological experiment does not allow the high temporal resolution to reduce excessive levels of illumination or minimize unnecessary oversampling to monitor long-term dynamics, it is still a challenging task to obtain good tracking results with coarsely sampled imaging data. To address this problem, we consider cell tracking problem as "stable matching problem" and propose a robust tracking method based on Voronoi partition which adapts parameters that need to be set according to the spatio-temporal characteristics of live cell imaging data such as cell population and migration. We demonstrate the performance improvement provided by the proposed method using numerical simulations and compare its performance with proximity-based tracking and nearest neighbor-based tracking.

show abstract

TrackPad: Software for semi-automated single-cell tracking and lineage annotation

Cited by 14 publications

References 16 publications

Cell-Projection Pumping of Fibroblast Contents into Osteosarcoma SAOS-2 Cells Correlates with Increased SAOS-2 Proliferation and Migration, and also with Altered Morphology

Cell-Projection Pumping of Fibroblast Contents into Osteosarcoma SAOS-2 Cells Correlates with Increased SAOS-2 Proliferation and Migration, and also with Altered Morphology

QuickPIV: Efficient 3D particle image velocimetry software applied to quantifying cellular migration during embryogenesis

Single cell tracking based on Voronoi partition via stable matching

Contact Info

Product

Resources

About