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Software tools for single-cell tracking and quantification of cellular and molecular properties

Nature Biotechnology volume 34, pages 703–706 (2016)Cite this article

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Continuous long-term single-cell quantification of cell behavior and molecular dynamics is essential to advance the understanding of molecular cell fate control1,2,3,4. Recent studies highlight the importance of continuous single-cell analysis also in mammalian systems, but lack of software tools for single-cell tracking and quantification suitable for long-term time-lapse microscopy experiments still impedes their analysis5. Although long-term imaging is possible, it involves numerous technical challenges. Monitoring processes, such as differentiation of individual cells, typically requires observation for up to weeks and more than ten generations with sufficient optical magnification, and thus numerous fields of view. Time-lapse acquisition must be frequent enough to track motile cells with changing morphologies and must be able to capture multiple fluorescence channels. This results in massive data volumes (up to millions of images per experiment) to be analyzed.

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Figure 1: Continuous single-cell quantification with tTt and qTfy.
Figure 2: Quantitative long-term time-lapse microscopy of Nanog transcription factor expression dynamics in embryonic stem cells.

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  • 13 July 2016

    The PDF file originally posted online was the main text file and not the supplementary file. In addition the software file was not posted. The errors have been corrected as of 13 July 2016.

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Acknowledgements

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.

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Author notes

  1. Oliver Hilsenbeck, Michael Schwarzfischer, Stavroula Skylaki, Fabian J Theis and Timm Schroeder: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule (ETH) Zurich, Basel, Switzerland

    Oliver Hilsenbeck, Stavroula Skylaki, Philipp S Hoppe, Dirk Loeffler, Konstantinos D Kokkaliaris, Simon Hastreiter, Eleni Skylaki, Max Endele, Martin Etzrodt & Timm Schroeder

  2. Research Unit Stem Cell Dynamics, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Neuherberg, Germany

    Oliver Hilsenbeck, Bernhard Schauberger, Philipp S Hoppe, Dirk Loeffler, Konstantinos D Kokkaliaris, Simon Hastreiter, Adam Filipczyk, Adrianus J J van den Berg, Max Endele & Timm Schroeder

  3. Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Neuherberg, Germany

    Oliver Hilsenbeck, Michael Schwarzfischer, Michael Strasser, Felix Buggenthin, Justin S Feigelman, Jan Krumsiek, Carsten Marr & Fabian J Theis

  4. Department of Mathematics, Technische Universität München, Garching, Germany

    Fabian J Theis

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Contributions

O.H. programmed tTt with B.S. and T.S. M.Sc. and S.S. designed and programmed qTfy with E.S. A.F. and S.Ha. performed mouse embryonic stem cell experiments. A.F., S.Ha., F.B., J.K., J.F., A.B., P.H., D.L., M.En., M.Et., M.St., D.L., K.K., C.M., F.T. and T.S. tested and contributed ideas to qTfy and tTt. F.T. and C.M. devised the computational concept of qTfy. T.S. planned and supervised the study and wrote the manuscript with O.H., M.Sc. and S.S. All authors commented on the manuscript.

Corresponding authors

Correspondence to Fabian J Theis or Timm Schroeder.

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The authors declare no competing financial interests.

Additional information

Editor's note: This article has been peer reviewed.

Integrated supplementary information

Supplementary Figure 1 tTt graphical user interface and data visualization.

(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).

Supplementary Figure 3 qTfy graphical user interface and data visualization.

(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.

Supplementary Figure 5 Fluorescence quantification by imaging versus flow cytometry.

(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).

Supplementary Figure 6 In silico background cells provide a reliable negative gate estimation.

(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 information

Supplementary Figures and Tables

Supplementary Figures 1–7 and Supplementary Tables 1 and 2 (PDF 1604 kb)

Supplementary Software (ZIP 37112 kb)

Supplementary Video 1

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)

Supplementary Video 2

Background estimation can be used to get correctly normalized fluorescence images. (MP4 2774 kb)

Supplementary Video 3

Long-term single-cell tracking and quantification of a single mESC colony. (MP4 3085 kb)

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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

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