This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Nature Communications Open Access 30 May 2022
Plant Methods Open Access 18 May 2022
Scientific Reports Open Access 17 February 2022
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Schroeder, T. Imaging stem-cell-driven regeneration in mammals. Nature 453, 345–351 (2008).
Etzrodt, M., Endele, M. & Schroeder, T. Quantitative single-cell approaches to stem cell research. Cell Stem Cell 15, 546–558 (2014).
Hoppe, P.S., Coutu, D.L. & Schroeder, T. Single-cell technologies sharpen up mammalian stem cell research. Nat. Cell Biol. 16, 919–927 (2014).
Sulston, J.E. & Horvitz, H.R. Post-embryonic cell lineages of the nematode, Caenorhabditis elegans. Dev. Biol. 56, 110–156 (1977).
Schroeder, T. Long-term single-cell imaging of mammalian stem cells. Nat. Methods 8 Suppl, S30–S35 (2011).
Meijering, E. Cell segmentation: 50 years down the road. IEEE Signal Process. Mag. 29, 140–145 (2012).
Maška, M. et al. A benchmark for comparison of cell tracking algorithms. Bioinformatics 30, 1609–1617 (2014).
Buggenthin, F. et al. An automatic method for robust and fast cell detection in bright field images from high-throughput microscopy. BMC Bioinformatics 14, 297 (2013).
Meijering, E., Dzyubachyk, O., Smal, I. & van Cappellen, W.A. Tracking in cell and developmental biology. Semin. Cell Dev. Biol. 20, 894–902 (2009).
Meijering, E., Dzyubachyk, O. & Smal, I. Methods for cell and particle tracking. Methods Enzymol. 504, 183–200 (2012).
Eliceiri, K.W. et al. Biological imaging software tools. Nat. Methods 9, 697–710 (2012).
Huth, J. et al. TimeLapseAnalyzer: multi-target analysis for live-cell imaging and time-lapse microscopy. Comput. Methods Programs Biomed. 104, 227–234 (2011).
Klein, J. et al. TLM-Tracker: software for cell segmentation, tracking and lineage analysis in time-lapse microscopy movies. Bioinformatics 28, 2276–2277 (2012).
de Chaumont, F. et al. Icy: an open bioimage informatics platform for extended reproducible research. Nat. Methods 9, 690–696 (2012).
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
Jaqaman, K. et al. Robust single-particle tracking in live-cell time-lapse sequences. Nat. Methods 5, 695–702 (2008).
Carpenter, A.E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).
Carpenter, A.E., Kamentsky, L. & Eliceiri, K.W. A call for bioimaging software usability. Nat. Methods 9, 666–670 (2012).
Rieger, M.A., Hoppe, P.S., Smejkal, B.M., Eitelhuber, A.C. & Schroeder, T. Hematopoietic cytokines can instruct lineage choice. Science 325, 217–218 (2009).
Eilken, H.M., Nishikawa, S. & Schroeder, T. Continuous single-cell imaging of blood generation from haemogenic endothelium. Nature 457, 896–900 (2009).
Hoppe, P.S. et al. Early myeloid lineage choice is not initiated by random PU.1 to GATA1 protein ratios. Nature (2016) http://dx.doi.org/10.1038/nature18320
Filipczyk, A. et al. Network plasticity of pluripotency transcription factors in embryonic stem cells. Nat. Cell Biol. 17, 1235–1246 (2015).
Ortega, F. et al. Oligodendrogliogenic and neurogenic adult subependymal zone neural stem cells constitute distinct lineages and exhibit differential responsiveness to Wnt signalling. Nat. Cell Biol. 15, 602–613 (2013).
Schwarzfischer, M. et al. Efficient fluorescence image normalization for time lapse movies. in ICSB 2011 Work. Microsc. Image Anal. with Appl. Biol. (2011).
Filipczyk, A. et al. Biallelic expression of nanog protein in mouse embryonic stem cells. Cell Stem Cell 13, 12–13 (2013).
Schneider, C.A., Rasband, W.S. & Eliceiri, K.W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).
Winter, M. et al. Vertebrate neural stem cell segmentation, tracking and lineaging with validation and editing. Nat. Protoc. 6, 1942–1952 (2011).
Okita, C., Sato, M. & Schroeder, T. Generation of optimized yellow and red fluorescent proteins with distinct subcellular localization. Biotechniques 36, 418–422, 424 (2004).
Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. A density-based algorithm for discovering clusters in large spatial databases with noise. in Proc. Second Int. Conf. Knowl. Discov. Data Min. 226–231 (1996).
Otsu, N. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man. Cybern. 9, 62–66 (1979).
Sahoo, P. Soltani, S. & Wong, A.K. A survey of thresholding techniques. Comput. Vis. Graph. Image Process. 41, 233–260 (1988).
This work was supported by the German Federal Ministry of Education and Research (BMBF), the European Research Council starting grant (Latent Causes) and the BioSysNet (Bavarian Research Network for Molecular Biosystems) to F.T., HFSP and the SNF to T.S., and the German Research Foundation (DFG) with SPPs 1395 (InKoMBio) to F.T., 1356 to C.M., F.T. and T.S. T.S., S.S. and O.H. acknowledge financial support for this project from SystemsX.ch.
The authors declare no competing financial interests.
Editor's note: This article has been peer reviewed.
Integrated supplementary information
(a) Part of experiment overview showing different overlapping fields of view (=positions), each with a resolution of 1388x1040 pixels. The selected position is highlighted in red and the number of tracked colonies starting in each position is shown in square brackets. (b) Selection of images for loading by time or imaging channel. Images of fluorescence channels are highlighted with colors. (c) Synchronous display of different imaging channels for inspection and cell tracking. Display of bright field image data (OV) includes colored overlay of signals from fluorescence channels (1,2). Circles indicate already tracked cells. Any set of imaging channels can be displayed in an arbitrary layout, and display settings such as color transformations can be set individually for each channel. (d) Controls for adjustment of image display, pedigree editing and statistics. (e) Example cellular pedigree. Colors qualitatively visualize cell adherence status and fluorescence in different channels for each timepoint. Cell death and loss of cell events are indicated by 'X' and '?' symbols respectively. A mESC pedigree is shown, but cell adherence and fluorescence properties were arbitrarily changed to illustrate multi-channel display possibilities of tTt.
Supplementary Figure 2 Accurate fluorescence image normalization is applied to achieve comparable cellular signal throughout the whole movie.
The background signal (c), which can vary over time, is estimated independently for each raw image (a) by filtering out cellular signal (b) and will be subtracted from its raw image. A final division by the time-independent gain function (e) derived by linear regression of actual pixel intensity and mean fluorescence over time (d) calibrates every pixel to a comparable level (f).
(a) The default visualization shows a tracked tree quantified with qTfy. (b) An additional context menu allows to change the appearance of every trajectory allowing to highlight specific cells of interest. Here, cells without fluorescence expression are highlighted in black, and cells switching on fluorescence are represented in red. (c) The Segmentation editor of qTfy allows to inspect and adjust the segmentation parameters or even draw cell outlines by hand. (d) The heat tree plot of qTfy provides additional possibilities for visualization and exploration of the quantified data. Here, the absolute intensities of the trajectories are plotted (color scheme from yellow/low to red/high intensity).
Supplementary Figure 4 Large-scale single-cell fluorescence quantification requires efficient computer assisted inspection and correction of automated segmentation results.
(a) Cell quantification based on non-normalized data will fail because of frequent mis-segmentation. (b) After normalization, an automatic segmentation approach might still fail (e.g. segmentation of the wrong object at 22h) but general trends will be visible. (c) After manual inspection of the data, accurate protein dynamics are observed.
(a) VENUS quantification of NanogVENUS expressing ESCs by imaging (n=3, one representative shown, number of cells = 193, 1980 and 532). Negative gate was determined by in silico background cells (number of cells = 10000 each, see Supplementary Information and Supplementary Fig. 6). (b) VENUS quantification by flow cytometry of same cell population (n=3, one representative shown, number of cells = 21285, 16500 and 8187). Negative gate determined by VENUS negative control ESCs (number of cells = 6351, 27135 and 8918). (c) Sensitivity (% of detected cells above the negative gate) is comparable between approaches (n=3 each).
(a) Cell areas of manually corrected cell segmentations of ESCs are used as reference distribution for in silico background cells (n=1,440). (b) Manually selected normalized background pixels show an intensity distribution centered on zero (n=220,057). (c) The distribution of in silico background cells can be referred as the amount of intensity which can be quantified in a control cell without any fluorescent labels. The 99% quantile is used as the negative gate (vertical line). Every signal above this line is regarded as cellular signal. Quantification of R1 control ESCs (without VENUS expression) based on mCHERRYnucmem segmentation leads to a similar negative gate (n=1513).
Supplementary Figure 7 Comparison of non-normalized (dashed line) and normalized (black solid line) fluorescence intensity distributions quantified by imaging to a reference distribution quantified with flow cytometry (red solid line, n=8846).
Every distribution has been standardized by dividing it by its mean, so all have the same mean equal to one. The comparison was repeated using three experiments (a: n=342, b: n=257, c: n=583).
Supplementary Figures 1–7 and Supplementary Tables 1 and 2 (PDF 1604 kb)
tTt enables efficient manual cell tracking as well as inspection and correction of existing tracked cell lineage trees (e.g. from auto-tracking programs) (MP4 31816 kb)
Background estimation can be used to get correctly normalized fluorescence images. (MP4 2774 kb)
Long-term single-cell tracking and quantification of a single mESC colony. (MP4 3085 kb)
About this article
Cite this article
Hilsenbeck, O., Schwarzfischer, M., Skylaki, S. et al. Software tools for single-cell tracking and quantification of cellular and molecular properties. Nat Biotechnol 34, 703–706 (2016). https://doi.org/10.1038/nbt.3626
This article is cited by
Plant Methods (2022)
Nature Communications (2022)
Scientific Reports (2022)
Transglutaminase 2 promotes tumorigenicity of colon cancer cells by inactivation of the tumor suppressor p53
Extracting neuronal activity signals from microscopy recordings of contractile tissue using B-spline Explicit Active Surfaces (BEAS) cell tracking
Scientific Reports (2021)