Summary: Live imaging studies give unparalleled insight into dynamic single cell behaviours and fate decisions. However, the challenge of reliably tracking single cells over long periods of time limits both the throughput and ease with which such studies can be performed. Here, we present NucliTrack, a cross platform solution for automatically segmenting, tracking and extracting features from fluorescently-labelled nuclei. NucliTrack performs similarly to other state-of-the-art cell tracking algorithms, but NucliTrack's interactive, graphical interface makes it significantly more user friendly. Availability: NucliTrack is available as a free, cross platform application, and open source Python package. Installation details and documentation are at: http://nuclitrack.readthedocs.io/en/latest / A video guide can be viewed online: https://www.youtube.com/watch?v=J6e0D9F-qSU Source code is available through Github: https://github.com/samocooper/nuclitrack . A Matlab toolbox is also available at: https://uk.mathworks.com/matlabcentral/fileexchange/61479-samocooper-nuclitrack-matlab. Contact: sam@socooper.com. Supplementary information: Supplementary data are available at Bioinformatics online

Bakal, C

Barr, AR

Cooper, S

Glen, R

Bioinformatics

SUMMARY: Live imaging studies give unparalleled insight into dynamic single cell behaviours and fate decisions. However, the challenge of reliably tracking single cells over long periods of time limits both the throughput and ease with which such studies can be performed. Here, we present NucliTrack, a cross platform solution for automatically segmenting, tracking and extracting features from fluorescently labelled nuclei. NucliTrack performs similarly to other state-of-the-art cell tracking algorithms, but NucliTrack's interactive, graphical interface makes it significantly more user friendly. AVAILABILITY AND IMPLEMENTATION: NucliTrack is available as a free, cross platform application and open source Python package. Installation details and documentation are at: http://nuclitrack.readthedocs.io/en/latest/ A video guide can be viewed online: https://www.youtube.com/watch?v=J6e0D9F-qSU Source code is available through Github: https://github.com/samocooper/nuclitrack. A Matlab toolbox is also available at: https://uk.mathworks.com/matlabcentral/fileexchange/61479-samocooper-nuclitrack-matlab. CONTACT: sam@socooper.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Institute of Cancer Research Repository

Bioimage informaticsNucliTrack: an integrated nuclei trackingapplicationSam Cooper1,2,*, Alexis R. Barr1, Robert Glen2 and Chris Bakal11Division of Cancer Biology, The Institute of Cancer Research, London SW3 6JB, UK and 2Department ofComputational Systems Medicine, Imperial College, South Kensington Campus, London SW7 2AZ, UK*To whom correspondence should be addressed.Associate Editor: Robert MurphyReceived on 14 February 2017; revised on 22 May 2017; editorial decision on 16 June 2017; accepted on 17 June 2017AbstractSummary: Live imaging studies give unparalleled insight into dynamic single cell behaviours andfate decisions. However, the challenge of reliably tracking single cells over long periods of time limitsboth the throughput and ease with which such studies can be performed. Here, we presentNucliTrack, a cross platform solution for automatically segmenting, tracking and extracting featuresfrom fluorescently labelled nuclei. NucliTrack performs similarly to other state-of-the-art cell trackingalgorithms, but NucliTrack’s interactive, graphical interface makes it significantly more user friendly.Availability and implementation: NucliTrack is available as a free, cross platform application andopen source Python package. Installation details and documentation are at: http://nuclitrack.readthedocs.io/en/latest/ A video guide can be viewed online: https://www.youtube.com/watch?v¼J6e0D9F-qSU Source code is available through Github: https://github.com/samocooper/nuclitrack. A Matlab toolbox is also available at: https://uk.mathworks.com/matlabcentral/fileexchange/61479-samocooper-nuclitrack-matlab.Contact: sam@socooper.comSupplementary information: Supplementary data are available at Bioinformatics online.1 IntroductionLive imaging studies are now allowing us to explore the previouslyshrouded world of mammalian cell signalling dynamics. For ex-ample, imaging populations of live single cells over several days hasrevealed how signalling dynamics can control key cell fate decisions(Cooper and Bakal, 2017). However, hindering the ease, time-periods and throughput with which live single-cell studies can beperformed, are challenges in automatically tracking fluorescentlylabelled objects (cells, nuclei or organelles) accurately over time peri-ods often exceeding several days, sometimes in highly motile cells(Cabantous et al., 2005).To date, the majority of cell tracking software relies on ‘frame-to-frame linking’ of segmented nuclei or cells (Meijering et al.,2012). In such methods, errors in segmentation, where, for example,either multiple nuclei are labelled as a single segment, or a nucleus ismissed over several timeframes resulting in a tracking gap, can se-verely compromise tracking results. To handle such difficulties,global optimization methods have now been developed (Milanet al., 2016). Yet, implementations of global optimizationapproaches to tracking in cell biology research have generally beenlimited to standalone packages, meaning that segmentation, trackinspection and track correction have to be performed in other soft-ware which is non-trivial for scientists with little programming ex-pertise (Hilsenbeck et al., 2016). Therefore, we have developedNucliTrack an integrated Python package that allows efficient seg-mentation, feature extraction and tracking of fluorescently labellednuclei or cells through an intuitive graphical interface (Fig. 1).Additionally, the interface also includes tools for inspecting, correct-ing and exporting data on tracked nuclei.2 ImplementationThe identification and segmentation of fluorescently labelled ob-jects in NucliTrack is based on marker controlled watershedVC The Author 2017. Published by Oxford University Press. 1This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permitsunrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.Bioinformatics, 2017, 1–3doi: 10.1093/bioinformatics/btx404Advance Access Publication Date: 20 June 2017Applications NoteDownloaded from https://academic.oup.com/bioinformatics/article-abstract/doi/10.1093/bioinformatics/btx404/3871473/NucliTrack-an-integrated-nuclei-trackingby Chester Beatty Research Institute useron 04 September 2017segmentation, built with the scikit image Python library (van derWalt et al., 2014). We utilized a novel Python application develop-ment library ‘Kivy’ to create an interface that allows users to inter-actively vary segmentation parameters (Virbel et al., 2011). Onceeffective parameters are chosen, segmentation is then performed onthe entire imaging sequence.In many types of image analysis workflows aimed at quantifyingcellular phenotypes, the segmentation step can result in errors,which must be handled by tracking algorithms. Moreover, cellsundergoing cell division must also be detected. To address this, weuse a probabilistic approach described by Magnusson et al. to per-form tracking (Magnusson et al., 2015). This requires training datato be selected that contains examples of any erroneous segmenta-tion, as well as normal, mitotic and post-mitotic cells. Training isperformed within the user interface prior to tracking. The trackingalgorithm then seeks to globally optimize the set of all tracks, by it-eratively adding the highest scoring track given the current set oftracks. Tracks are scored with a cost function which penalizes largemovements, gaps between frames, and passage through segmentswhich are unlikely to be nuclei, whilst highly scoring tracks thatminimize movement between frames, include few gaps and passthrough segments likely to be nuclei. A dynamic programming tech-nique is then used to find the highest scoring track that is added tothe set of all tracks. Importantly, in identifying the highest scoringtrack, swaps with neighbouring tracks may be performed, thus mini-mizing the likelihood that a local, rather than a global, optimum isfound.After successful tracking of objects, tracks can be edited withinthe user interface. Here, three windows showing: (i) colour-codedtracked segments; (ii) the original video; and (iii) feature values overthe full length of a selected track (in the form of a line plot overtime), allow the user to easily analyze and inspect tracks for errors,or anomalous results. Moreover, using a set of tools with keyboardhotkeys the user can efficiently navigate the video, manually correcterrors in tracking, and add flags to mark specific events, before ex-porting results. Throughout NucliTrack we have also incorporateddata management steps, such that intermediate steps are saved in theHDF5 file format, through the Python h5py package (Collette,2013). Parameters are stored in a separate HDF5 file meaning thatthe segmentation and training stages can be skipped when analyzingsubsequent movies. A function also exists in the Python packagethat accommodates for batch processing of videos using the HDF5parameter file.To characterize the performance of the global optimization ap-proach to automated cell tracking we tested NucliTrack against abenchmark set of fluorescently labelled nuclei videos (Ma!ska et al.,2014; Svoboda et al., 2009). Here NucliTrack performed similarlyto other global optimization approaches (Supplementary Methods).The performance of NucliTrack was limited by the more basicsegmentation procedures we adopt, however we believe having anintuitive and interactive workflow is significantly more importantfor users with less technical expertise. Labeled images, segmentedin alternative programs may also be imported in the file loadingstep.NucliTrack is an easy to use package for tracking and extractingdata from fluorescently labelled cells or nuclei, in significantlyhigher throughput than manual tracking can achieve. Importantly,by allowing users to quickly inspect and correct tracking data, near100% accuracy can be achieved, which is critical in quantifying cellfate decisions. For example, NucliTrack allowed us to study howDNA damage influences the proliferation-quiescence decision inthousands of cells, tracked for hundreds of frames, over multipledays (Barr et al., 2017). Finally, by coupling established packagesfor data processing in Python, with more recent solutions to datamanagement (h5py), graphical user interface development (kivy)and image processing (skimage), this software also provides an ex-ample of how user friendly, scientific software may be efficiently de-veloped in an open source environment.FundingS.C. is supported by the NIHR Imperial BRC, and the STRATiGRAD gradu-ate training program. C.B. and A.R.B. are funded by a BBSRC Strategic LoLagrant [BB/M00354X/1], C.B. is also funded by a Cancer Research UKProgramme Foundation Award [C37275/1A20146].Conflict of Interest: none declared.ReferencesBarr,A.R. et al. (2017) DNA damage during S-phase mediates theproliferation-quiescence decision in the subsequent G1 via p21 expression.Nat. Commun., 8, 14728.Cabantous,S. et al. (2005) Protein tagging and detection with engineered self-assembling fragments of GFP.Nat. Biotechnol., 23, 102–107.Collette,A. (2013) Python and HDF5: Unlocking Scientific Data ‘O’ReillyMedia’.Cooper,S. and Bakal,C. (2017) Accelerating live single-cell signalling studies.Trends Biotechnol, 35, 422–433.Hilsenbeck,O. et al. (2016) Software tools for single-cell tracking and quantifi-cation of cellular and molecular properties.Nat. Biotechnol., 34, 703–706.Magnusson,K.E.G. et al. (2015) Global linking of cell tracks using the Viterbialgorithm. IEEE Trans. Med. Imaging, 34, 911–929.Fig. 1. The Nuclitrack application workflow: This figure shows the four main user interface windows used to track nuclei in Nuclitrack: (1) File loading: In this inter-face the user can browse files and load the time series imaging data, as well as selecting file names for storing tracking data; (2) Segmentation: The sliders onthis interface allow the user to interactively choose segmentation parameters for identifying cell nuclei; (3) Training data selection: As tracking uses a probabilisticalgorithm, examples of single nuclei, nuclei about to enter and exit mitosis, as well as incorrectly segmented nuclei must be given; (4) Track visualization, correc-tion and export: In this interface the user can explore the tracking results, and view tracking data in the graph window. Additionally functions exist to correcttracks. Finally, the user can export single cell data from either all tracks or a subset of selected tracks2 S.Cooper et al.Downloaded from https://academic.oup.com/bioinformatics/article-abstract/doi/10.1093/bioinformatics/btx404/3871473/NucliTrack-an-integrated-nuclei-trackingby Chester Beatty Research Institute useron 04 September 2017Ma!ska,M. et al. (2014) A benchmark for comparison of cell tracking algo-rithms. Bioinformatics, 30, 1609–1617.Meijering,E. et al. (2012) Methods for cell and particle tracking. MethodsEnzymol., 504, 183–200.Milan,A. et al. (2016) MOT16: A Benchmark for Multi-Object Tracking.arXiv.Svoboda,D. et al. (2009) Generation of digital phantoms of cell nuclei andsimulation of image formation in 3D image cytometry. Cytometry Part A,75, 494–509.Virbel,M. et al. (2011) Kivy-A Framework for Rapid Creation of InnovativeUser Interfaces.Van der Walt,S. et al. (2014) scikit-image: image processing in Python.NucliTrack 3Downloaded from https://academic.oup.com/bioinformatics/article-abstract/doi/10.1093/bioinformatics/btx404/3871473/NucliTrack-an-integrated-nuclei-trackingby Chester Beatty Research Institute useron 04 September 2017

NucliTrack: an integrated nuclei tracking application.

Live imaging studies give unparalleled insight into dynamic single cell behaviours and fate decisions. However, the challenge of reliably tracking single cells over long periods of time limits both the throughput and ease with which such studies can be performed. Here, we present NucliTrack, a cross platform solution for automatically segmenting, tracking and extracting features from fluorescently labelled nuclei. NucliTrack performs similarly to other state-of-the-art cell tracking algorithms, but NucliTrack’s interactive, graphical interface makes it significantly more user friendly

Spiral - Imperial College Digital Repository

Bioimage informaticsNucliTrack: an integrated nuclei trackingapplicationSam Cooper1,2,*, Alexis R. Barr1, Robert Glen2 and Chris Bakal11Division of Cancer Biology, The Institute of Cancer Research, London SW3 6JB, UK and 2Department ofComputational Systems Medicine, Imperial College, South Kensington Campus, London SW7 2AZ, UK*To whom correspondence should be addressed.Associate Editor: Robert MurphyReceived on 14 February 2017; revised on 22 May 2017; editorial decision on 16 June 2017; accepted on 17 June 2017AbstractSummary: Live imaging studies give unparalleled insight into dynamic single cell behaviours andfate decisions. However, the challenge of reliably tracking single cells over long periods of time limitsboth the throughput and ease with which such studies can be performed. Here, we presentNucliTrack, a cross platform solution for automatically segmenting, tracking and extracting featuresfrom fluorescently labelled nuclei. NucliTrack performs similarly to other state-of-the-art cell trackingalgorithms, but NucliTrack’s interactive, graphical interface makes it significantly more user friendly.Availability and implementation: NucliTrack is available as a free, cross platform application andopen source Python package. Installation details and documentation are at: http://nuclitrack.readthedocs.io/en/latest/ A video guide can be viewed online: https://www.youtube.com/watch?v¼J6e0D9F-qSU Source code is available through Github: https://github.com/samocooper/nuclitrack. A Matlab toolbox is also available at: https://uk.mathworks.com/matlabcentral/fileexchange/61479-samocooper-nuclitrack-matlab.Contact: sam@socooper.comSupplementary information: Supplementary data are available at Bioinformatics online.1 IntroductionLive imaging studies are now allowing us to explore the previouslyshrouded world of mammalian cell signalling dynamics. For ex-ample, imaging populations of live single cells over several days hasrevealed how signalling dynamics can control key cell fate decisions(Cooper and Bakal, 2017). However, hindering the ease, time-periods and throughput with which live single-cell studies can beperformed, are challenges in automatically tracking fluorescentlylabelled objects (cells, nuclei or organelles) accurately over time peri-ods often exceeding several days, sometimes in highly motile cells(Cabantous et al., 2005).To date, the majority of cell tracking software relies on ‘frame-to-frame linking’ of segmented nuclei or cells (Meijering et al.,2012). In such methods, errors in segmentation, where, for example,either multiple nuclei are labelled as a single segment, or a nucleus ismissed over several timeframes resulting in a tracking gap, can se-verely compromise tracking results. To handle such difficulties,global optimization methods have now been developed (Milanet al., 2016). Yet, implementations of global optimizationapproaches to tracking in cell biology research have generally beenlimited to standalone packages, meaning that segmentation, trackinspection and track correction have to be performed in other soft-ware which is non-trivial for scientists with little programming ex-pertise (Hilsenbeck et al., 2016). Therefore, we have developedNucliTrack an integrated Python package that allows efficient seg-mentation, feature extraction and tracking of fluorescently labellednuclei or cells through an intuitive graphical interface (Fig. 1).Additionally, the interface also includes tools for inspecting, correct-ing and exporting data on tracked nuclei.2 ImplementationThe identification and segmentation of fluorescently labelled ob-jects in NucliTrack is based on marker controlled watershedVC The Author 2017. Published by Oxford University Press. 3320This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permitsunrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.Bioinformatics, 33(20), 2017, 3320–3322doi: 10.1093/bioinformatics/btx404Advance Access Publication Date: 20 June 2017Applications NoteDownloaded from https://academic.oup.com/bioinformatics/article-abstract/33/20/3320/3871473by University of Cambridge useron 24 May 2018segmentation, built with the scikit image Python library (van derWalt et al., 2014). We utilized a novel Python application develop-ment library ‘Kivy’ to create an interface that allows users to inter-actively vary segmentation parameters (Virbel et al., 2011). Onceeffective parameters are chosen, segmentation is then performed onthe entire imaging sequence.In many types of image analysis workflows aimed at quantifyingcellular phenotypes, the segmentation step can result in errors,which must be handled by tracking algorithms. Moreover, cellsundergoing cell division must also be detected. To address this, weuse a probabilistic approach described by Magnusson et al. to per-form tracking (Magnusson et al., 2015). This requires training datato be selected that contains examples of any erroneous segmenta-tion, as well as normal, mitotic and post-mitotic cells. Training isperformed within the user interface prior to tracking. The trackingalgorithm then seeks to globally optimize the set of all tracks, by it-eratively adding the highest scoring track given the current set oftracks. Tracks are scored with a cost function which penalizes largemovements, gaps between frames, and passage through segmentswhich are unlikely to be nuclei, whilst highly scoring tracks thatminimize movement between frames, include few gaps and passthrough segments likely to be nuclei. A dynamic programming tech-nique is then used to find the highest scoring track that is added tothe set of all tracks. Importantly, in identifying the highest scoringtrack, swaps with neighbouring tracks may be performed, thus mini-mizing the likelihood that a local, rather than a global, optimum isfound.After successful tracking of objects, tracks can be edited withinthe user interface. Here, three windows showing: (i) colour-codedtracked segments; (ii) the original video; and (iii) feature values overthe full length of a selected track (in the form of a line plot overtime), allow the user to easily analyze and inspect tracks for errors,or anomalous results. Moreover, using a set of tools with keyboardhotkeys the user can efficiently navigate the video, manually correcterrors in tracking, and add flags to mark specific events, before ex-porting results. Throughout NucliTrack we have also incorporateddata management steps, such that intermediate steps are saved in theHDF5 file format, through the Python h5py package (Collette,2013). Parameters are stored in a separate HDF5 file meaning thatthe segmentation and training stages can be skipped when analyzingsubsequent movies. A function also exists in the Python packagethat accommodates for batch processing of videos using the HDF5parameter file.To characterize the performance of the global optimization ap-proach to automated cell tracking we tested NucliTrack against abenchmark set of fluorescently labelled nuclei videos (Maska et al.,2014; Svoboda et al., 2009). Here NucliTrack performed similarlyto other global optimization approaches (Supplementary Methods).The performance of NucliTrack was limited by the more basicsegmentation procedures we adopt, however we believe having anintuitive and interactive workflow is significantly more importantfor users with less technical expertise. Labeled images, segmentedin alternative programs may also be imported in the file loadingstep.NucliTrack is an easy to use package for tracking and extractingdata from fluorescently labelled cells or nuclei, in significantlyhigher throughput than manual tracking can achieve. Importantly,by allowing users to quickly inspect and correct tracking data, near100% accuracy can be achieved, which is critical in quantifying cellfate decisions. For example, NucliTrack allowed us to study howDNA damage influences the proliferation-quiescence decision inthousands of cells, tracked for hundreds of frames, over multipledays (Barr et al., 2017). Finally, by coupling established packagesfor data processing in Python, with more recent solutions to datamanagement (h5py), graphical user interface development (kivy)and image processing (skimage), this software also provides an ex-ample of how user friendly, scientific software may be efficiently de-veloped in an open source environment.FundingS.C. is supported by the NIHR Imperial BRC, and the STRATiGRAD gradu-ate training program. C.B. and A.R.B. are funded by a BBSRC Strategic LoLagrant [BB/M00354X/1], C.B. is also funded by a Cancer Research UKProgramme Foundation Award [C37275/1A20146].Conflict of Interest: none declared.ReferencesBarr,A.R. et al. (2017) DNA damage during S-phase mediates theproliferation-quiescence decision in the subsequent G1 via p21 expression.Nat. Commun., 8, 14728.Cabantous,S. et al. (2005) Protein tagging and detection with engineered self-assembling fragments of GFP. Nat. Biotechnol., 23, 102–107.Collette,A. (2013) Python and HDF5: Unlocking Scientific Data ‘O’ReillyMedia’.Cooper,S. and Bakal,C. (2017) Accelerating live single-cell signalling studies.Trends Biotechnol, 35, 422–433.Hilsenbeck,O. et al. (2016) Software tools for single-cell tracking and quantifi-cation of cellular and molecular properties. Nat. Biotechnol., 34, 703–706.Magnusson,K.E.G. et al. (2015) Global linking of cell tracks using the Viterbialgorithm. IEEE Trans. Med. Imaging, 34, 911–929.Fig. 1. The Nuclitrack application workflow: This figure shows the four main user interface windows used to track nuclei in Nuclitrack: (1) File loading: In this inter-face the user can browse files and load the time series imaging data, as well as selecting file names for storing tracking data; (2) Segmentation: The sliders onthis interface allow the user to interactively choose segmentation parameters for identifying cell nuclei; (3) Training data selection: As tracking uses a probabilisticalgorithm, examples of single nuclei, nuclei about to enter and exit mitosis, as well as incorrectly segmented nuclei must be given; (4) Track visualization, correc-tion and export: In this interface the user can explore the tracking results, and view tracking data in the graph window. Additionally functions exist to correcttracks. Finally, the user can export single cell data from either all tracks or a subset of selected tracksNucliTrack 3321Downloaded from https://academic.oup.com/bioinformatics/article-abstract/33/20/3320/3871473by University of Cambridge useron 24 May 2018Maska,M. et al. (2014) A benchmark for comparison of cell tracking algo-rithms. Bioinformatics, 30, 1609–1617.Meijering,E. et al. (2012) Methods for cell and particle tracking. MethodsEnzymol., 504, 183–200.Milan,A. et al. (2016) MOT16: A Benchmark for Multi-Object Tracking.arXiv.Svoboda,D. et al. (2009) Generation of digital phantoms of cell nuclei andsimulation of image formation in 3D image cytometry. Cytometry Part A,75, 494–509.Virbel,M. et al. (2011) Kivy-A Framework for Rapid Creation of InnovativeUser Interfaces.Van der Walt,S. et al. (2014) scikit-image: image processing in Python.3322 S.Cooper et al.Downloaded from https://academic.oup.com/bioinformatics/article-abstract/33/20/3320/3871473by University of Cambridge useron 24 May 2018

NucliTrack: an integrated nuclei tracking application

Abstract
               
                  Summary
                  Live imaging studies give unparalleled insight into dynamic single cell behaviours and fate decisions. However, the challenge of reliably tracking single cells over long periods of time limits both the throughput and ease with which such studies can be performed. Here, we present NucliTrack, a cross platform solution for automatically segmenting, tracking and extracting features from fluorescently labelled nuclei. NucliTrack performs similarly to other state-of-the-art cell tracking algorithms, but NucliTrack’s interactive, graphical interface makes it significantly more user friendly.
               
               
                  Availability and implementation
                  NucliTrack is available as a free, cross platform application and open source Python package. Installation details and documentation are at: http://nuclitrack.readthedocs.io/en/latest/ A video guide can be viewed online: https://www.youtube.com/watch?v=J6e0D9F-qSU Source code is available through Github: https://github.com/samocooper/nuclitrack. A Matlab toolbox is also available at: https://uk.mathworks.com/matlabcentral/fileexchange/61479-samocooper-nuclitrack-matlab.
               
               
                  Supplementary information
                  Supplementary data are available at Bioinformatics online.
               </jats:sec

Sam Cooper

Alexis R Barr

Robert Glen

Chris Bakal

Crossref

Apollo (Cambridge)

Bioimage informaticsNucliTrack: an integrated nuclei trackingapplicationSam Cooper1,2,*, Alexis R. Barr1, Robert Glen2 and Chris Bakal11Division of Cancer Biology, The Institute of Cancer Research, London SW3 6JB, UK and 2Department ofComputational Systems Medicine, Imperial College, South Kensington Campus, London SW7 2AZ, UK*To whom correspondence should be addressed.Associate Editor: Robert MurphyReceived on 14 February 2017; revised on 22 May 2017; editorial decision on 16 June 2017; accepted on 17 June 2017AbstractSummary: Live imaging studies give unparalleled insight into dynamic single cell behaviours andfate decisions. However, the challenge of reliably tracking single cells over long periods of time limitsboth the throughput and ease with which such studies can be performed. Here, we presentNucliTrack, a cross platform solution for automatically segmenting, tracking and extracting featuresfrom fluorescently labelled nuclei. NucliTrack performs similarly to other state-of-the-art cell trackingalgorithms, but NucliTrack’s interactive, graphical interface makes it significantly more user friendly.Availability and implementation: NucliTrack is available as a free, cross platform application andopen source Python package. Installation details and documentation are at: http://nuclitrack.readthedocs.io/en/latest/ A video guide can be viewed online: https://www.youtube.com/watch?v¼J6e0D9F-qSU Source code is available through Github: https://github.com/samocooper/nuclitrack. A Matlab toolbox is also available at: https://uk.mathworks.com/matlabcentral/fileexchange/61479-samocooper-nuclitrack-matlab.Contact: sam@socooper.comSupplementary information: Supplementary data are available at Bioinformatics online.1 IntroductionLive imaging studies are now allowing us to explore the previouslyshrouded world of mammalian cell signalling dynamics. For ex-ample, imaging populations of live single cells over several days hasrevealed how signalling dynamics can control key cell fate decisions(Cooper and Bakal, 2017). However, hindering the ease, time-periods and throughput with which live single-cell studies can beperformed, are challenges in automatically tracking fluorescentlylabelled objects (cells, nuclei or organelles) accurately over time peri-ods often exceeding several days, sometimes in highly motile cells(Cabantous et al., 2005).To date, the majority of cell tracking software relies on ‘frame-to-frame linking’ of segmented nuclei or cells (Meijering et al.,2012). In such methods, errors in segmentation, where, for example,either multiple nuclei are labelled as a single segment, or a nucleus ismissed over several timeframes resulting in a tracking gap, can se-verely compromise tracking results. To handle such difficulties,global optimization methods have now been developed (Milanet al., 2016). Yet, implementations of global optimizationapproaches to tracking in cell biology research have generally beenlimited to standalone packages, meaning that segmentation, trackinspection and track correction have to be performed in other soft-ware which is non-trivial for scientists with little programming ex-pertise (Hilsenbeck et al., 2016). Therefore, we have developedNucliTrack an integrated Python package that allows efficient seg-mentation, feature extraction and tracking of fluorescently labellednuclei or cells through an intuitive graphical interface (Fig. 1).Additionally, the interface also includes tools for inspecting, correct-ing and exporting data on tracked nuclei.2 ImplementationThe identification and segmentation of fluorescently labelled ob-jects in NucliTrack is based on marker controlled watershedVC The Author 2017. Published by Oxford University Press. 3320This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permitsunrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.Bioinformatics, 33(20), 2017, 3320–3322doi: 10.1093/bioinformatics/btx404Advance Access Publication Date: 20 June 2017Applications NoteDownloaded from https://academic.oup.com/bioinformatics/article-abstract/33/20/3320/3871473 by University of Cambridge user on 22 January 2020segmentation, built with the scikit image Python library (van derWalt et al., 2014). We utilized a novel Python application develop-ment library ‘Kivy’ to create an interface that allows users to inter-actively vary segmentation parameters (Virbel et al., 2011). Onceeffective parameters are chosen, segmentation is then performed onthe entire imaging sequence.In many types of image analysis workflows aimed at quantifyingcellular phenotypes, the segmentation step can result in errors,which must be handled by tracking algorithms. Moreover, cellsundergoing cell division must also be detected. To address this, weuse a probabilistic approach described by Magnusson et al. to per-form tracking (Magnusson et al., 2015). This requires training datato be selected that contains examples of any erroneous segmenta-tion, as well as normal, mitotic and post-mitotic cells. Training isperformed within the user interface prior to tracking. The trackingalgorithm then seeks to globally optimize the set of all tracks, by it-eratively adding the highest scoring track given the current set oftracks. Tracks are scored with a cost function which penalizes largemovements, gaps between frames, and passage through segmentswhich are unlikely to be nuclei, whilst highly scoring tracks thatminimize movement between frames, include few gaps and passthrough segments likely to be nuclei. A dynamic programming tech-nique is then used to find the highest scoring track that is added tothe set of all tracks. Importantly, in identifying the highest scoringtrack, swaps with neighbouring tracks may be performed, thus mini-mizing the likelihood that a local, rather than a global, optimum isfound.After successful tracking of objects, tracks can be edited withinthe user interface. Here, three windows showing: (i) colour-codedtracked segments; (ii) the original video; and (iii) feature values overthe full length of a selected track (in the form of a line plot overtime), allow the user to easily analyze and inspect tracks for errors,or anomalous results. Moreover, using a set of tools with keyboardhotkeys the user can efficiently navigate the video, manually correcterrors in tracking, and add flags to mark specific events, before ex-porting results. Throughout NucliTrack we have also incorporateddata management steps, such that intermediate steps are saved in theHDF5 file format, through the Python h5py package (Collette,2013). Parameters are stored in a separate HDF5 file meaning thatthe segmentation and training stages can be skipped when analyzingsubsequent movies. A function also exists in the Python packagethat accommodates for batch processing of videos using the HDF5parameter file.To characterize the performance of the global optimization ap-proach to automated cell tracking we tested NucliTrack against abenchmark set of fluorescently labelled nuclei videos (Maska et al.,2014; Svoboda et al., 2009). Here NucliTrack performed similarlyto other global optimization approaches (Supplementary Methods).The performance of NucliTrack was limited by the more basicsegmentation procedures we adopt, however we believe having anintuitive and interactive workflow is significantly more importantfor users with less technical expertise. Labeled images, segmentedin alternative programs may also be imported in the file loadingstep.NucliTrack is an easy to use package for tracking and extractingdata from fluorescently labelled cells or nuclei, in significantlyhigher throughput than manual tracking can achieve. Importantly,by allowing users to quickly inspect and correct tracking data, near100% accuracy can be achieved, which is critical in quantifying cellfate decisions. For example, NucliTrack allowed us to study howDNA damage influences the proliferation-quiescence decision inthousands of cells, tracked for hundreds of frames, over multipledays (Barr et al., 2017). Finally, by coupling established packagesfor data processing in Python, with more recent solutions to datamanagement (h5py), graphical user interface development (kivy)and image processing (skimage), this software also provides an ex-ample of how user friendly, scientific software may be efficiently de-veloped in an open source environment.FundingS.C. is supported by the NIHR Imperial BRC, and the STRATiGRAD gradu-ate training program. C.B. and A.R.B. are funded by a BBSRC Strategic LoLagrant [BB/M00354X/1], C.B. is also funded by a Cancer Research UKProgramme Foundation Award [C37275/1A20146].Conflict of Interest: none declared.ReferencesBarr,A.R. et al. (2017) DNA damage during S-phase mediates theproliferation-quiescence decision in the subsequent G1 via p21 expression.Nat. Commun., 8, 14728.Cabantous,S. et al. (2005) Protein tagging and detection with engineered self-assembling fragments of GFP. Nat. Biotechnol., 23, 102–107.Collette,A. (2013) Python and HDF5: Unlocking Scientific Data ‘O’ReillyMedia’.Cooper,S. and Bakal,C. (2017) Accelerating live single-cell signalling studies.Trends Biotechnol, 35, 422–433.Hilsenbeck,O. et al. (2016) Software tools for single-cell tracking and quantifi-cation of cellular and molecular properties. Nat. Biotechnol., 34, 703–706.Magnusson,K.E.G. et al. (2015) Global linking of cell tracks using the Viterbialgorithm. IEEE Trans. Med. Imaging, 34, 911–929.Fig. 1. The Nuclitrack application workflow: This figure shows the four main user interface windows used to track nuclei in Nuclitrack: (1) File loading: In this inter-face the user can browse files and load the time series imaging data, as well as selecting file names for storing tracking data; (2) Segmentation: The sliders onthis interface allow the user to interactively choose segmentation parameters for identifying cell nuclei; (3) Training data selection: As tracking uses a probabilisticalgorithm, examples of single nuclei, nuclei about to enter and exit mitosis, as well as incorrectly segmented nuclei must be given; (4) Track visualization, correc-tion and export: In this interface the user can explore the tracking results, and view tracking data in the graph window. Additionally functions exist to correcttracks. Finally, the user can export single cell data from either all tracks or a subset of selected tracksNucliTrack 3321Downloaded from https://academic.oup.com/bioinformatics/article-abstract/33/20/3320/3871473 by University of Cambridge user on 22 January 2020Maska,M. et al. (2014) A benchmark for comparison of cell tracking algo-rithms. Bioinformatics, 30, 1609–1617.Meijering,E. et al. (2012) Methods for cell and particle tracking. MethodsEnzymol., 504, 183–200.Milan,A. et al. (2016) MOT16: A Benchmark for Multi-Object Tracking.arXiv.Svoboda,D. et al. (2009) Generation of digital phantoms of cell nuclei andsimulation of image formation in 3D image cytometry. Cytometry Part A,75, 494–509.Virbel,M. et al. (2011) Kivy-A Framework for Rapid Creation of InnovativeUser Interfaces.Van der Walt,S. et al. (2014) scikit-image: image processing in Python.3322 S.Cooper et al.Downloaded from https://academic.oup.com/bioinformatics/article-abstract/33/20/3320/3871473 by University of Cambridge user on 22 January 2020

NucliTrack: An integrated nuclei tracking application

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NucliTrack: An integrated nuclei tracking application

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