DNA flow cytometry for detection of genomic instability as a cancer precursor in the gastrointestinal tract.


Journal

Methods in cell biology
ISSN: 0091-679X
Titre abrégé: Methods Cell Biol
Pays: United States
ID NLM: 0373334

Informations de publication

Date de publication:
2024
Historique:
medline: 6 5 2024
pubmed: 6 5 2024
entrez: 5 5 2024
Statut: ppublish

Résumé

One of the earliest applications of flow cytometry was the measurement of DNA content in cells. This method is based on the ability to stain DNA in a stoichiometric manner (i.e., the amount of stain is directly proportional to the amount of DNA within the cell). For more than 40years, a number of studies have consistently demonstrated the utility of DNA flow cytometry as a potential diagnostic and/or prognostic tool in patients with most epithelial tumors, including pre-invasive lesions (such as dysplasia) in the gastrointestinal tract. However, its availability as a clinical test has been limited to few medical centers due to the requirement for fresh tissue in earlier studies and perceived technical demands. However, more recent studies have successfully utilized formalin-fixed paraffin-embedded (FFPE) tissue to generate high-quality DNA content histograms, demonstrating the feasibility of this methodology. This review summarizes step-by-step methods on how to perform DNA flow cytometry using FFPE tissue and analyze DNA content histograms based on the published consensus guidelines in order to assist in the diagnosis and/or risk stratification of many different epithelial tumors, with particular emphasis on dysplasia associated with Barrett's esophagus and inflammatory bowel disease.

Identifiants

pubmed: 38705603
pii: S0091-679X(24)00044-X
doi: 10.1016/bs.mcb.2024.02.013
pii:
doi:

Substances chimiques

DNA 9007-49-2

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

25-49

Informations de copyright

Copyright © 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Auteurs

Won-Tak Choi (WT)

Department of Pathology, University of California at San Francisco, San Francisco, CA, United States. Electronic address: won-tak.choi@ucsf.edu.

Peter S Rabinovitch (PS)

Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, United States.

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Classifications MeSH