Rare Cell Detection
Rare Cell Detection - Related Videos
09:25
Detection and Enrichment of Rare Antigen-specific B Cells for Analysis of Phenotype and Function
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Cited by 36 •
2017
A simple yet effective method that employs magnetic nanoparticles to detect and enrich antigen-reactive B cells for functional and phenotypic analysis is described.
14:06
Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Cited by 10 •
2012
Pooled DNA sequencing is a fast and cost-effective strategy to detect rare variants associated with complex phenotypes in large cohorts. Here we describe the computational analysis of pooled, next-generation sequencing of 32 cancer-related genes using the SPLINTER software package. This method is scalable, and applicable to any phenotype of interest.
07:50
Chip-Based Digital PCR to Detect Rare Transcript Variants Using a Nanofluidic Chip
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2025
This video demonstrates chip-based digital PCR — a variation of the digital PCR technique that is useful in detecting rare transcript variants. The PCR reaction is partitioned into the chambers of a nanofluidic chip, each of which acts as an independent reaction. The detection of fluorescence signals from the chambers with amplified targets confirms the presence of rare transcript variants in the sample.
11:11
Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
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Cited by 23 •
2017
This manuscript describes a technique for detecting mutations of low frequency in ctDNA, ER-Seq. This method is differentiated by its unique use of two-directional error correction, a special background filter, and efficient molecular acquirement.
10:36
Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Cited by 13 •
2018
Next-generation sequencing (NGS) is a powerful tool for genomic characterization that is limited by the high error rate of the platform (~0.5–2.0%). We describe our methods of error-corrected sequencing that allow us to obviate the NGS error rate and detect mutations at variant allele fractions as rare as 0.0001.
FAQs
Selective enrichment reduces the relative abundance of background cells before the target population is measured. This gives fluorescence imaging, flow-based analysis, or molecular detection a clearer signal to evaluate. By concentrating the cells of interest while retaining material needed for identification, enrichment can improve the ability to characterize populations that would otherwise be obscured by abundant cells.
A detectable signal alone does not establish that the observed event is the intended cell type. Rare cell detection therefore pairs sensitive readouts with identity confirmation, using approaches such as fluorescence imaging, flow-based analysis, or molecular detection. This combination helps distinguish target cells from background events and supports more reliable characterization of unusual or heterogeneous populations.
These readouts provide different ways to recognize and examine scarce cells. Fluorescence imaging can support visual identification, flow-based analysis can evaluate cells within a larger measured population, and molecular detection can provide identity-related information. Using one or more of these approaches after sample preparation and enrichment helps address the difficulty of finding target cells among many background cells.
Bulk measurements summarize signals across a population and may conceal cells that occur at very low frequencies. Rare cell detection focuses analytical attention on those uncommon cells, allowing researchers to examine their presence and characteristics separately from the dominant population. This distinction is important when cellular heterogeneity or an unusual subpopulation carries biological significance that an averaged measurement could miss.
A typical workflow begins with biological sample preparation, followed by selective enrichment when needed. The processed sample is then examined with a sensitive readout, such as fluorescence imaging, flow-based analysis, or molecular detection. Finally, the resulting events or signals are evaluated to distinguish target cells from background and to characterize their identity or biological relevance.
The approach can be applied to several uncommon populations, including circulating tumor cells, rare immune populations, and unusual microbial cells. These examples span disease research, immunology, and microbiology, showing that the method is not limited to one cell type. Its value comes from making biologically meaningful minority populations accessible for further characterization.
Rare cell detection is useful when biologically important changes may occur in a small cellular subset rather than across the entire sample. In disease research, it can support earlier disease assessment by revealing uncommon target cells. Repeated or comparative analyses can also help monitor biological changes in research or clinical samples, including shifts involving cellular heterogeneity.
It enables investigators to examine minority populations that differ from the dominant cells in a sample. Identifying and characterizing these uncommon groups can reveal cellular heterogeneity that bulk measurements may average away. In biology, this supports more detailed investigation of population structure and can connect rare cellular findings with disease research or other biological changes.