Multiparameter Single-Cell Characterization of Ovarian Intratumor Heterogeneity

Kristin G. Beaumont, Christina Andreou, Ethan Ellis, Robert Sebra

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Cancer is a complex disease rooted in heterogeneity, which is the phenomenon of individual cells, tissues, or patients having distinct phenotypic and/or genetic characteristics. Observed divergent disease etiology is likely rooted, at least in part, in tumor heterogeneity and the classification of distinct and important subpopulations of cells within the tumor and its associated microenvironment has remained a technical challenge. Standard next-generation sequencing of bulk tumor tissue provides an overall average genetic profile of the sample, and masks contributions from individual cells and minor populations of cells, particularly in heterogeneous samples. Only with the advent of single-cell analysis and sequencing technologies has it become possible to characterize key contributions of cellular subpopulations in order to more comprehensively characterize disease. This chapter describes a method to generate linked phenotypic and genotypic data at single-cell resolution using a real-time single-cell resolved platform. Specifically, the example method provided here is used to link cellular growth kinetics and expression of a prognostic marker protein, CA-125, in cells derived from ovarian cancer patients with their single-cell genomic profiles, but the method is translatable to other cell types and phenotypes of interest.

Original languageEnglish
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Number of pages12
StatePublished - 2022

Publication series

NameMethods in Molecular Biology
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029


  • Ovarian cancer
  • Single-cell DNA Seq
  • Single-cell RNA Seq
  • Single-cell analysis
  • Tumor heterogeneity


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