Dissecting phenotypic transitions in metastatic disease via photoconversion-based isolation
- University of Pennsylvania, United States
Abstract
Cancer patients often harbor occult metastases, a potential source of relapse that is targetable only through systemic therapy. Studies of this occult fraction have been limited by a lack of tools with which to isolate discrete cells on spatial grounds. We developed PIC-IT, a photoconversion-based isolation technique allowing efficient recovery of cell clusters of any size – including single-metastatic cells – which are largely inaccessible otherwise. In a murine pancreatic cancer model, transcriptional profiling of spontaneously arising microcolonies revealed phenotypic heterogeneity, functionally reduced propensity to proliferate and enrichment for an inflammatory-response phenotype associated with NF-κB/AP-1 signaling. Pharmacological inhibition of NF-κB depleted microcolonies but had no effect on macrometastases, suggesting microcolonies are particularly dependent on this pathway. PIC-IT thus enables systematic investigation of metastatic heterogeneity. Moreover, the technique can be applied to other biological systems in which isolation and characterization of spatially distinct cell populations is not currently feasible.
Data availability
Sequencing data have been deposited in GEO under the accession code GSE158078.
Article and author information
Author details
Funding
National Cancer Institute (CA229803)
- Ben Z Stanger
National Institutes of Health (DK083355)
- Ben Z Stanger
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (#804643) of the University of Pennsylvania.
Copyright
© 2021, Sela et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Dissecting phenotypic transitions in metastatic disease via photoconversion-based isolation
eLife 10:e63270.
https://doi.org/10.7554/eLife.63270
