A simple method for analyzing competitive growth of multiple cell types in xenograft tumors
- 1Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America.
- 2Department of Surgery, University of Virginia, Charlottesville, Virginia, United States of America.
Abstract
Low take rates and inter-tumor variability in growth rates can limit the effectiveness of mouse xenograft models when comparing between groups. To address this problem we developed a simple method to compare multiple cell types within a single mixed xenograft. Individual cell lines or clones were transduced with a lentiviral vector that includes a unique PCR tag, allowing the use of qPCR to determine the proportion of each tagged cell type within a mixed xenograft tumor. We generated vectors with six distinct PCR tags, and two different selectable markers, and have optimized the approach for determining their relative proportions within a mix. An initial pre-amplification step is used to increase the amount of material for subsequent qPCR reactions. This also removes the bulk of the genomic DNA, increasing the specificity of the qPCR step. Samples are then used for qPCR with specific pairs of primers that distinguish between each of the individual PCR tags, and the relative proportion of each tag is determined relative to that in the starting mix. We have tested this approach for in vitro growth of mixed cell cultures and in an orthotopic cecal xenograft model using a human colon cancer cell line. Since each individual tumor is initiated with a mix of cells, multiple tumors within a single animal can be analyzed separately, and overall tumor size is not important. Similarly, multiple metastatic lesions from the same animal can be analyzed individually. Thus, each tumor provides a direct comparison between individually tagged cell lines or clones. This low throughput “bar-coding” approach is simple and cost effective and has the potential to reduce the number of animals needed for xenograft experiments.
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BibTeX
@article{melhuish2026simple,
title = {{A simple method for analyzing competitive growth of multiple cell types in xenograft tumors}},
author = {Tiffany A Melhuish and Sara J Adair and Olivia S Pemberton and Todd W Bauer and David Wotton},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0357415},
url = {https://doi.org/10.1371/journal.pone.0357415},
note = {CC BY},
}
RIS
TY - JOUR
TI - A simple method for analyzing competitive growth of multiple cell types in xenograft tumors
AU - Tiffany A Melhuish
AU - Sara J Adair
AU - Olivia S Pemberton
AU - Todd W Bauer
AU - David Wotton
PY - 2026
DA - 2026/10/15
JO - PLOS One
DO - 10.1371/journal.pone.0357415
UR - https://doi.org/10.1371/journal.pone.0357415
AB - Low take rates and inter-tumor variability in growth rates can limit the effectiveness of mouse xenograft models when comparing between groups. To address this problem we developed a simple method to compare multiple cell types within a single mixed xenograft. Individual cell lines or clones were transduced with a lentiviral vector that includes a unique PCR tag, allowing the use of qPCR to determine the proportion of each tagged cell type within a mixed xenograft tumor. We generated vectors with six distinct PCR tags, and two different selectable markers, and have optimized the approach for determining their relative proportions within a mix. An initial pre-amplification step is used to increase the amount of material for subsequent qPCR reactions. This also removes the bulk of the genomic DNA, increasing the specificity of the qPCR step. Samples are then used for qPCR with specific pairs of primers that distinguish between each of the individual PCR tags, and the relative proportion of each tag is determined relative to that in the starting mix. We have tested this approach for in vitro growth of mixed cell cultures and in an orthotopic cecal xenograft model using a human colon cancer cell line. Since each individual tumor is initiated with a mix of cells, multiple tumors within a single animal can be analyzed separately, and overall tumor size is not important. Similarly, multiple metastatic lesions from the same animal can be analyzed individually. Thus, each tumor provides a direct comparison between individually tagged cell lines or clones. This low throughput “bar-coding” approach is simple and cost effective and has the potential to reduce the number of animals needed for xenograft experiments.
SN - 1932-6203
ER -