TraCurate: Efficiently curating cell tracks

Abstract TraCurate is an open-source software tool to curate and manually annotate cell tracking data from time-lapse microscopy. Although many studies of cellular behaviour require high-quality, long-term observations of single cells across several generations, automated tracking of individual cells is often imperfect and typically yields fragmented results that still contain many errors. TraCurate supports the user to efficiently curate and extract complete cell tracks and genealogies from a variety of cell tracking data. Source code and binary packages for TraCurate and all related tools are freely available for Linux, macOS, and Windows at https://tracurate.gitlab.io/ .

[1]  Alexa Kiss,et al.  CellTracker (not only) for dummies , 2016, Bioinform..

[2]  Nico Scherf,et al.  On the Ontological Foundations of Cellular Development , 2020, bioRxiv.

[3]  Michael Cross,et al.  Elucidating functional heterogeneity in hematopoietic progenitor cells: a combined experimental and modeling approach. , 2014, Experimental hematology.

[4]  Citlali Pérez Campos,et al.  High-speed panoramic light-sheet microscopy reveals global endodermal cell dynamics , 2013, Nature Communications.

[5]  Johannes Schindelin,et al.  TrackMate: An open and extensible platform for single-particle tracking. , 2017, Methods.

[6]  Romain F. Laine,et al.  Automated cell tracking using StarDist and TrackMate , 2020, F1000Research.

[7]  Lennart Martens,et al.  Community standards for open cell migration data , 2019, bioRxiv.

[8]  Nathalie Harder,et al.  A benchmark for comparison of cell tracking algorithms , 2014, Bioinform..

[9]  Abbas Shirinifard,et al.  The cell behavior ontology: describing the intrinsic biological behaviors of real and model cells seen as active agents , 2014, Bioinform..

[10]  Ingo Roeder,et al.  On the symmetry of siblings: automated single-cell tracking to quantify the behavior of hematopoietic stem cells in a biomimetic setup. , 2012, Experimental hematology.

[11]  Timm Schroeder,et al.  Imaging stem-cell-driven regeneration in mammals , 2008, Nature.

[12]  Fabian J. Theis,et al.  fastER: a user‐friendly tool for ultrafast and robust cell segmentation in large‐scale microscopy , 2017, Bioinform..

[13]  Nico Scherf,et al.  Assisting the Machine Paradigms for Human-Machine Interaction in Single Cell Tracking , 2013, Bildverarbeitung für die Medizin.

[14]  Nathalie Harder,et al.  An Objective Comparison of Cell Tracking Algorithms , 2017, Nature Methods.

[15]  Alan M. Moses,et al.  YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells , 2019, Bioinform..

[16]  Stavroula Skylaki,et al.  Challenges in long-term imaging and quantification of single-cell dynamics , 2016, Nature Biotechnology.

[17]  Réka Hollandi,et al.  OpSeF IV: Open source Python framework for segmentation of biomedical images , 2020 .

[18]  D. Rapoport,et al.  A Novel Validation Algorithm Allows for Automated Cell Tracking and the Extraction of Biologically Meaningful Parameters , 2011, PloS one.

[19]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[20]  Nico Scherf,et al.  Ontology patterns for the representation of quality changes of cells in time , 2019, J. Biomed. Semant..

[21]  Alan Ruttenberg,et al.  The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability , 2016, J. Biomed. Semant..

[22]  B. S. Manjunath,et al.  Biological imaging software tools , 2012, Nature Methods.

[23]  Alan Edelman,et al.  Julia: A Fresh Approach to Numerical Computing , 2014, SIAM Rev..

[24]  Srinivas C. Turaga,et al.  In Toto Imaging and Reconstruction of Post-Implantation Mouse Development at the Single-Cell Level , 2018, Cell.

[25]  Stuart E. Berg,et al.  Segmenting and Tracking Multiple Dividing Targets Using ilastik. , 2016, Advances in anatomy, embryology, and cell biology.

[26]  Anne E Carpenter,et al.  CellProfiler 3.0: Next-generation image processing for biology , 2018, PLoS biology.

[27]  Fred A. Hamprecht,et al.  ilastik: interactive machine learning for (bio)image analysis , 2019, Nature Methods.

[28]  Anne E Carpenter,et al.  CellProfiler: image analysis software for identifying and quantifying cell phenotypes , 2006, Genome Biology.

[29]  J. Wallingford.  The 200-year effort to see the embryo , 2019, Science.

[30]  Réka Hollandi,et al.  OpSeF: Open Source Python Framework for Collaborative Instance Segmentation of Bioimages , 2020, bioRxiv.

[31]  Johannes E. Schindelin,et al.  Fiji: an open-source platform for biological-image analysis , 2012, Nature Methods.

[32]  Eugene W. Myers,et al.  Cell Detection with Star-convex Polygons , 2018, MICCAI.

[33]  I. Glauche,et al.  Quantitative Cell Cycle Analysis Based on an Endogenous All-in-One Reporter for Cell Tracking and Classification , 2017, Cell reports.

[34]  William Graf,et al.  Deep learning for cellular image analysis , 2019, Nature Methods.

[35]  Lassi Paavolainen,et al.  nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer , 2020, Cell systems.

[36]  Philipp J. Keller,et al.  Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data , 2014, Nature Methods.

[37]  Edward Pao,et al.  Caliban: Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning , 2019, bioRxiv.