Clonal analysis of mixed cell populations as an alternative to flow cytometry for epigenetic cellular identity 3808
Year: 2025
Type: article
Abstract: Description To better understand mixed cell populations (e.g., PBMCs), flow cytometry is the standard for immunophenotyping. However, it has significant drawbacks, including limited accuracy, sample storage needs, apoptotic cell loss, nonspecific binding, autofluorescence, and extensive preparation time. Sorted cells can then undergo genetic and epigenetic analyses, which only further delays data acquisition while increasing overall cost per sample. To overcome these disadvantages, we have developed an in-house protocol utilizing clonal analysis to obtain genetic and epigenetic data while being able to correlate mixed cell populations from the original sample. This approach uses the Ion Torrent Chef’s clonal bead amplification step and S5 prime sequencer. During emulsion PCR, a single molecule of DNA is amplified onto an ion sphere particle (ISP) and loaded onto a sequencing chip. Each bead represents a single molecule from a single cell, and cell-specific epigenetic patterns allow accurate estimation of mixed cell populations. This method is particularly useful for understanding regulatory T cell (Treg) populations in cell immunotherapy and cell manufacturing such as differentiation success following drug treatments. Clonal analysis is faster, cheaper, and easier than flow cytometry, without introducing biases or compromising data quality. Topic Categories Technological Innovations in Immunology (TECH)
Source: The Journal of Immunology
Author: Collin M. Hudzik
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: bronze
Funder: National Science Foundation
Awards: Collaborative Research: Macrophages: Guardians of amphibian skin antifungal defenses, Collaborative Research: Macrophages: Guardians of amphibian skin antifungal defenses, Collaborative Research: NSF-BIO/BBSRC: The amphibian skin microbial-immune interface and its impact on infection outcome
OpenAlex ID: W4416446720
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