Cancer Cell Detection
Cancer Cell Detection - Related Videos
05:17
Detection of Targetable Alterations in Non-small Cell Lung Cancer using Next-generation Sequencing
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2025
This protocol describes an automated, ISO15189-accredited next-generation sequencing workflow for detecting targetable genomic alterations in non-small cell lung cancer (NSCLC) formalin-fixed paraffin-embedded tissues.
09:57
Harnessing the DNA Dye-triggered Side Population Phenotype to Detect and Purify Cancer Stem Cells from Biological Samples
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Cited by 4 •
2017
Methods allowing the characterization and isolation of stem cell populations from biological samples are critical for the advance of stem cell-targeted treatments in cancer and beyond. Here, we provide a detailed protocol for cancer stem cell isolation using the dye-triggered side population phenotype.
04:25
In Vitro Adhesion Assay to Detect the Adherence of Cancer Cells to Neutrophil Extracellular Traps
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2025
In this video, we demonstrate an assay utilizing fluorescence microscopy to detect the adhesion of cancer cells to neutrophil extracellular traps (NETs). In the test wells, there is a notable increase in the number of fluorescently labeled cancer cells adhering to NETs, whereas wells treated with DNase exhibit reduced cancer cell adhesion to NETs.
07:17
Production and Detection of Reactive Oxygen Species (ROS) in Cancers
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Cited by 278 •
2011
Here we propose simple methods to test and evaluate the presence of reactive oxygen species in cells.
03:50
ROS Detection with DCFH-DA Staining: Measuring Total ROS in Colorectal Cancer Cell Lines
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2023
This video describes the protocol for detecting total cellular reactive oxygen species (ROS) using 2',7'-dichlorodihydrofluorescein diacetate (DCFH-DA). This method helps visualize ROS localization in the adherent cells using a fluorescence microscope and further quantify ROS intensity with a fluorescence plate reader.
FAQs
Different detection signals answer different biological questions. Morphology reveals abnormal cellular appearance, whereas tumor-associated biomarkers and altered protein expression provide molecular or phenotypic evidence. Genetic changes add another layer of characterization. Combining these readouts can improve confidence when one feature alone does not clearly distinguish malignant from normal cells.
The target depends on the biological sample and the information sought. Tissue analysis can examine a primary tumor, while blood-based analysis may focus on circulating tumor cells. Detection of minimal residual disease addresses whether cancer remains after treatment, and response monitoring examines changes over time. The same general approach can therefore support distinct disease assessments.
Detection sensitivity indicates how effectively a method reveals cancer-related signals, while specificity concerns how well it distinguishes malignant cells from normal cells. Both properties matter because a technique may detect relevant cells yet provide limited discrimination, or distinguish cell types without capturing all target cells. Improving these characteristics strengthens diagnosis and monitoring.
Selection begins with the sample and the information sought. Microscopy can examine morphology, immunostaining can assess tumor-associated biomarkers or protein expression, flow cytometry can analyze cells, and molecular assays can investigate genetic changes. Matching the technique to the available biological signal helps produce information suited to diagnosis, monitoring, or treatment planning.
Results can do more than indicate that abnormal cells are present. They may help characterize a tumor through morphology, biomarkers, protein expression, or genetic changes; identify circulating tumor cells; reveal minimal residual disease; and track treatment responses. These outputs connect detection with disease monitoring, treatment planning, and the development of personalized cancer therapies.
By revealing tumor-associated biomarkers, altered protein expression, or genetic changes, detection methods can contribute to more precise tumor characterization. Monitoring circulating tumor cells, minimal residual disease, or treatment responses adds information about disease status over time. Together, these measurements support research that connects biological findings with treatment planning and personalized therapeutic development.