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In vivo cell-cycle profiling in xenograft tumors by quantitative intravital microscopy

Nature Methods volume 12, pages 577–585 (2015)Cite this article

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Abstract

Quantification of cell-cycle state at a single-cell level is essential to understand fundamental three-dimensional (3D) biological processes such as tissue development and cancer. Analysis of 3D in vivo images, however, is very challenging. Today's best practice, manual annotation of select image events, generates arbitrarily sampled data distributions, which are unsuitable for reliable mechanistic inferences. Here, we present an integrated workflow for quantitative in vivo cell-cycle profiling. It combines image analysis and machine learning methods for automated 3D segmentation and cell-cycle state identification of individual cell-nuclei with widely varying morphologies embedded in complex tumor environments. We applied our workflow to quantify cell-cycle effects of three antimitotic cancer drugs over 8 d in HT-1080 fibrosarcoma xenografts in living mice using a data set of 38,000 cells and compared the induced phenotypes. In contrast to results with 2D culture, observed mitotic arrest was relatively low, suggesting involvement of additional mechanisms in their antitumor effect in vivo.

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Figure 1: Overview of experimental setup and image analysis.
Figure 2: Automatic segmentation of cell nuclei.
Figure 3: Automatic identification of cell-cycle state.
Figure 4: Quantitative analysis of drug response to paclitaxel treatment at a single-cell level.
Figure 5: Pharmacodynamics of antimitotic drugs in the HT-1080 xenograft system.
Figure 6: Antimitotic drugs induce different mitotic and interphase phenotypes.

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Acknowledgements

S.F. was supported by a research fellowship (FL 820/1-1) from the DFG (Deutsche Forschungsgemeinschaft). This project was supported by US National Institutes of Health grants R01-CA164448, S10RR0266360 and PO1-CA139980. We thank P. Choi for helpful discussions. We are grateful for the support of The Nikon Imaging Center at Harvard Medical School. We thank K. Krukenberg (Harvard Medical School) for the MCF7 and T47D cell lines stably expressing H2B-GFP; P. Keller (Howard Hughes Medical Institute, Janelia Research Campus) for Drosophila, mouse and zebrafish data sets; M. Sebas for surgical implantation of the DSCs; and J. Moore for assistance with flow cytometry. The FUCCI viral particles were a kind gift from P. Jorgensen, A. Tzur and M. Chung (Harvard Medical School) generated with vectors kindly provided by the laboratory of A. Miyawaki (RIKEN Brain Science Institute). The H2B-CFP construct was obtained from A. Loewer (Max Delbrueck Center).

Author information

Author notes

  1. Gaudenz Danuser

    Present address: Present address: Department of Cell Biology, The University of Texas Southwestern Medical Center, Dallas, Texas, USA.,

  2. Deepak R Chittajallu and Stefan Florian: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA

    Deepak R Chittajallu & Gaudenz Danuser

  2. Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA

    Stefan Florian, Ralph Weissleder & Timothy J Mitchison

  3. Center for Systems Biology, Massachusetts General Hospital, Boston, Massachusetts, USA

    Rainer H Kohler, Yoshiko Iwamoto & Ralph Weissleder

  4. Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, Colorado, USA

    James D Orth

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Contributions

D.R.C. designed and developed the automated image analysis framework. S.F. initiated and coordinated the project and designed the experiments. S.F. and R.H.K. performed the mouse experiments. S.F. performed the spheroid experiments. D.R.C. and S.F. analyzed all experiments and wrote the manuscript, R.H.K. developed the grid-based imaging setup for longitudinal observations. J.D.O. generated the stable HT-1080 cell line. Y.I. performed the immunohistochemistry staining. R.W., G.D. and T.J.M. initiated the collaboration and helped edit the manuscript.

Corresponding authors

Correspondence to Stefan Florian, Ralph Weissleder or Gaudenz Danuser.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Segmentation accuracy in undiluted xenograft tumors

Original images and segmentation results for a full density, undiluted xenograft HT-1080 tumor expressing H2B CFP and FUCCI are shown. The local signal to background thresholding algorithm provided in the software was used to obtain the initial foreground-background segmentation for these datasets.

Supplementary Figure 2 Segmentation accuracy for varying levels of cell density

Cell density is estimated as the percentage of voxels belonging to the foreground/cell-nuclei in the histone channel wherein the foreground mask is computed using the locally adaptive Poisson-based minimum error thresholding method used in our nuclei segmentation algorithm. Shown below the plot are 3D volume visualizations of a few volumes in our test dataset along with their cell densities.

Supplementary Figure 3 Segmentation accuracy in different biological systems acquired with multiple microscopy setups

The datasets in (a)-(c) were kindly provided by Philipp Keller and were also analyzed by his group in Fig. 3 of Amat et al., 201420. (a) Segmentation analysis of a stage 6 Drosophila embryo expressing His2Av-mRFP1 acquired on a Zeiss Lightsheet Z.1 microscope (timepoint 500 of the dataset), (b) a zebrafish embryo during early gastrulation expressing H2B-mCherry acquired on a Zeiss LSM 710 laser-scanning microscope (timepoint 0 of the dataset) and (c) a mouse embryo expressing H2B GFP acquired on a SimView light sheet microscope (timepoint 0 of the dataset) (d)-(f) Segmentation results for cancer cell line spheroids grown from (d) HT-1080, (e) T47D and (f) MCF7 cells and expressing H2B GFP. The LoG response thresholding and the local signal to background ratio thresholding algorithms provided in the software were chosen to obtain the initial foreground-background segmentation for the datasets in panels (a) and (b-f), respectively.

Supplementary Figure 4 Validation of cell-cycle quantification in vivo: histology

Twelve mice were injected with 2 million HT-1080 cells into the flank and subcutaneous tumors were allowed to grow for 3 weeks. Then, 3 mice each were either left untreated, or injected with a single dose of 40 mg/kg paclitaxel i.v. and sacrificed after 1, 4 or 7 days, respectively. Tumors were fixed and sections were stained with an antibody against the mitotic marker phospho-histone H3. The mitotic index (% mitotic cells) was quantified at 6 positions in each tumor. Note that the increase in mitotic index is relatively low compared to a 2D tissue culture environment and similar to, though slightly lower, than the data in Fig. 5b. The slightly lower mitotic index obtained through histology is probably due to the fact that the immunohistochemistry staining counts host cells as well, while the FUCCI imaging system is more accurate in this regard, labeling only tumor cells.

Supplementary Figure 5 Validation of cell-cycle quantification in 3D culture: flow cytometry

(a) a comparison of the FUCCI system and the DNA content quantification approach for determination of the cell cycle distribution. Note that they are not equivalent: the FUCCI/H2B combination cannot discriminate between S and G2, while the DNA content method does not separate G2 and M (one 4N peak). (b) HT-1080 spheroids were treated with 2 µM paclitaxel or the corresponding concentration of DMSO as control and the cell cycle distribution was assessed by spinning disk confocal microscopy of three spheroids from each group followed by software based automated analysis using our computational framework. (c) Then, all spheroids were dispersed into a single-cell suspension by a 1 min treatment with trypsin, stained with DyeCyle Violet dye and the DNA content was analyzed. Note that the changes in the G1 and S/G2/M compartments are almost identical with both methods. ~1500 cells per condition were analyzed in (b) and 5000 cells per condition were analyzed in (c).

Supplementary Figure 6 Montage of all cells analyzed from mice treated with paclitaxel

Cells from the mice treated with paclitaxel, as analyzed in Fig. 5, were arranged as in Fig. 4e to allow visual inspection of segmentation/classification accuracy and cell morphology by timepoint. A gap of two or more days between the imaged timepoints is highlighted by two oblique lines intersecting the axis. To see the cell thumbnails at full resolution, we recommend looking at the electronic version.

Supplementary Figure 7 Montage of all cells analyzed from mice treated with eribulin

Cells from the second mouse treated with eribulin, as analyzed in Fig. 5, were arranged as in Fig. 4e to allow visual inspection of segmentation/classification accuracy and cell morphology by timepoint. A gap of two or more days between the imaged timepoints is highlighted by two oblique lines intersecting the axis. To see the cell thumbnails at full resolution, we recommend looking at the electronic version.

Supplementary Figure 8 Montage of all cells analyzed from mice treated with ispinesib

Cells from the mice treated with ispinesib, as analyzed in Fig. 5, were arranged as in Fig. 4e to allow visual inspection of segmentation/classification accuracy and cell morphology by timepoint. A gap of two or more days between the imaged timepoints is highlighted by two oblique lines intersecting the axis. To see the cell thumbnails at full resolution, we recommend looking at the electronic version.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 and Supplementary Tables 1–13 (PDF 2133 kb)

Supplementary Software

InvivoCytometer 2.0 The software implementation of our proposed segmentation and cell cycle classification algorithms. Included: MATLAB source code, documentation, sample data (ZIP 147667 kb)

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Chittajallu, D., Florian, S., Kohler, R. et al. In vivo cell-cycle profiling in xenograft tumors by quantitative intravital microscopy. Nat Methods 12, 577–585 (2015). https://doi.org/10.1038/nmeth.3363

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