Clinical & Experimental Metastasis

, Volume 35, Issue 1–2, pp 77–86 | Cite as

Automated quantitative image analysis for ex vivo metastasis assays reveals differing lung composition requirements for metastasis suppression by KISS1

  • Eric D. Young
  • Kyle Strom
  • Ashley F. Tsue
  • Joseph L. Usset
  • Seth MacPherson
  • John T. McGuire
  • Danny R. Welch
Technical Notes in Metastasis Research


Imaging is broadly used in biomedical research, but signal variation complicates automated analysis. Using the Pulmonary Metastasis Assay (PuMA) to study metastatic colonization by the metastasis suppressor KISS1, we cultured GFP-expressing melanoma cells in living mouse lung ex vivo for 3 weeks. Epifluorescence images of cells were used to measure growth, creating large datasets which were time consuming and challenging to quantify manually due to scattering of light from outside the focal plane. To address these challenges, we developed an automated workflow to standardize the measurement of disseminated cancer cell growth by applying statistical quality control to remove unanalyzable images followed and a filtering algorithm to quantify only in-focus cells. Using this tool, we demonstrate that expression of the metastasis suppressor KISS1 does not suppress growth of melanoma cells in the PuMA, in contrast to the robust suppression of lung metastasis observed in vivo. This result may suggest that a factor required for metastasis suppression is present in vivo but absent in the PuMA, or that KISS1 suppresses lung metastasis at a step in the metastatic cascade not tested by the PuMA. Together, these data provide a new tool for quantification of metastasis assays and further insight into the mechanism of KISS1 mediated metastasis suppression in the lung.


Metastasis assay Pulmonary metastasis assay PuMA Lung metastasis KISS1 Quantitative fluorescent imaging 



The authors acknowledge Drs. Chand Khanna, DVM, PhD, Michael Lizardo, PhD and Arnulfo Mendoza, DVM for their training in the PuMA. The authors also acknowledge support from the Biostatistics and Informatics Shared Resource of the KU Cancer Center.

Author contributions

Conceived and designed experiments: EDY, KS, DRW. Analyzed data: EDY, AFT, JLU, SM, JTM. Wrote first draft of the manuscript: EDY. Contributed to manuscript writing: EDY, KS, AFT, JLU, DRW. Agree with manuscript results and conclusions: EDY, KS, AFT, JLU, SM, JTM, DRW. Developed the structure and arguments for the paper: EDY, KS, JLU, DRW. Made critical revisions and approved final version of the manuscript: EDY, KS, AFT, JLU, JTM, DRW.


The authors received support from the following organizations: Melanoma Research Foundation (EDY), National Cancer Institute 1F30CA216998 (EDY), CA134981 (DRW) and CA168524 (DRW), the National Foundation for Cancer Research (DRW) and the Hall Family Professorship in Molecular Medicine (DRW).

Supplementary material

10585_2018_9882_MOESM1_ESM.docx (11 kb)
Supplementary material 1 (DOCX 11 KB)
10585_2018_9882_MOESM2_ESM.tif (29.6 mb)
Supplementary material 2 (TIF 30350 KB)


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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Cancer BiologyUniversity of Kansas Medical CenterKansas CityUSA
  2. 2.Department of Civil and Environmental EngineeringVirginia TechBlacksburgUSA
  3. 3.Department of BiostatisticsUniversity of Kansas Medical CenterKansas CityUSA
  4. 4.40DigitsKansas CityUSA
  5. 5.The University of Kansas Cancer CenterKansas CityUSA

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