Effect of Antigen Retrieval on Genomic DNA From Immunodissected Samples

Posted on 23.09.2022 - 00:11

Immunohistochemical (IHC) staining is an established technique for visualizing proteins in tissue sections for research studies and clinical applications. IHC is increasingly used as a targeting strategy for procurement of labeled cells via tissue microdissection, including immunodissection, computer-aided laser dissection (CALD), expression microdissection (xMD), and other techniques. The initial antigen retrieval (AR) process increases epitope availability and improves staining characteristics; however, the procedure can damage DNA. To better understand the effects of AR on DNA quality and quantity in immunodissected samples, both clinical specimens (KRAS gene mutation positive cases) and model system samples (lung cancer patient-derived xenograft tissue) were subjected to commonly employed AR methods (heat induced epitope retrieval [HIER], protease digestion) and the effects on DNA were assessed by Qubit, fragment analysis, quantitative PCR, digital droplet PCR (ddPCR), library preparation, and targeted sequencing. The data showed that HIER resulted in optimal IHC staining characteristics, but induced significant damage to DNA, producing extensive fragmentation and decreased overall yields. However, neither of the AR methods combined with IHC prevented ddPCR amplification of small amplicons and gene mutations were successfully identified from immunodissected clinical samples. The results indicate for the first time that DNA recovered from immunostained slides after standard AR and IHC processing can be successfully employed for genomic mutation analysis via ddPCR and next-generation sequencing (NGS) short-read methods.


Johann, Donald J.; Shin, Ik Jae; Roberge, Adam; Laun, Sarah; Peterson, Erich A.; Liu, Meei; et al. (2022): Effect of Antigen Retrieval on Genomic DNA From Immunodissected Samples. SAGE Journals. Collection. https://doi.org/10.25384/SAGE.c.6211675.v1
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Journal of Histochemistry & Cytochemistry


Donald J. Johann
Ik Jae Shin
Adam Roberge
Sarah Laun
Erich A. Peterson
Meei Liu
Matthew A. Steliga
Jason Muesse
Michael R. Emmert-Buck
Michael A. Tangrea
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