ArticleScientific reports2021
Label-free microfluidic enrichment of cancer cells from non-cancer cells in ascites.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- Label-free detection of ovarian cancer cells in ascites-related cell models using digital holographic flow cytometry.Biomedical optics express · 2026Article
- High-throughput Genome Wide CRISPR Knock Out mechanical sort identifies genes driving metastatic cancer cell softening.bioRxiv : the preprint server for biology · 2026Article
- Label-Free Cancer Detection Methods Based on Biophysical Cell Phenotypes.Bioengineering (Basel, Switzerland) · 2025Review
- Characterization, Enrichment, and Computational Modeling of Cross-Linked Actin Networks in Transformed Trabecular Meshwork Cells.Investigative ophthalmology & visual science · 2025Article
- Immunomagnetic enrichment coupled to PAX8/TP53 molecular pathology approach increases sensitivity in the detection of ovarian cancer cells in ascites.Frontiers in molecular biosciences · 2025Article
- Characterization, enrichment, and computational modeling of cross-linked actin networks in trabecular meshwork cells.bioRxiv : the preprint server for biology · 2024Article
- New trends in diagnosing and treating ovarian cancer using nanotechnology.Frontiers in bioengineering and biotechnology · 2023Review
- Scaling microfluidic throughput with flow-balanced manifolds to simply control devices with multiple inlets and outlets.Biomicrofluidics · 2022Article
- Phenotypic heterogeneity and cooperation in the metastatic cascade.Oncoscience · 2022Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
Abstract
The isolation of a patient's metastatic cancer cells is the first, enabling step toward treatment of that patient using modern personalized medicine techniques. Whereas traditional standard-of-care approaches select treatments for cancer patients based on the histological classification of cancerous tissue at the time of diagnosis, personalized medicine techniques leverage molecular and functional analysis of a patient's own cancer cells to select treatments with the highest likelihood of being effective. Unfortunately, the pure populations of cancer cells required for these analyses can be difficult to acquire, given that metastatic cancer cells typically reside in fluid containing many different cell populations. Detection and analyses of cancer cells therefore require separation from these contaminating cells. Conventional cell sorting approaches such as Fluorescence Activated Cell Sorting or Magnetic Activated Cell Sorting rely on the presence of distinct surface markers on cells of interest which may not be known nor exist for cancer applications. In this work, we present a microfluidic platform capable of label-free enrichment of tumor cells from the ascites fluid of ovarian cancer patients. This approach sorts cells based on differences in biomechanical properties, and therefore does not require any labeling or other pre-sort interference with the cells. The method is also useful in the cases when specific surface markers do not exist for cells of interest. In model ovarian cancer cell lines, the method was used to separate invasive subtypes from less invasive subtypes with an enrichment of ~ sixfold. In ascites specimens from ovarian cancer patients, we found the enrichment protocol resulted in an improved purity of P53 mutant cells indicative of the presence of ovarian cancer cells. We believe that this technology could enable the application of personalized medicine based on analysis of liquid biopsy patient specimens, such as ascites from ovarian cancer patients, for quick evaluation of metastatic disease progression and determination of patient-specific treatment.
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