Digital PCR quantification of hematopoietic chimerism by insertion/deletion: A personalized selection for different chimerism status.

Abstract

Background: Clinical decision-making after allogeneic stem cell transplantation (HSCT) is partially based on hematopoietic chimerism analysis. Short tandem repeat (STR), the current gold standard for quantitative chimerism analysis, has limited sensitivity. Digital polymerase chain reaction (dPCR) combines precise quantification and high reproducibility with excellent sensitivity (usually ≤ 0.1%) in a wide measurement range. However, the reported dPCR-based chimerism detection methods were developed in non-Chinese cohorts and may not be applicable in a Chinese population.

Methods: To achieve higher sensitivity and accuracy, we first screened out 14 insertions/deletions (indel) loci with high individual recognition rates in Asian populations based on the literature and NCBI. Then, we established a dPCR detection system for routine chimerism assessment ("dPCR-chimerism system") in the Chinese transplant population. We compared the consistencies between STR and dPCR in patient samples.

Results: The newly established dPCR-chimerism system has a high coverage (12 pairs of autosomes), a sensitivity of 0.01%, and an excellent linearity of 0.016%-50%. For dual-donor samples, there was a strong correlation between STR and dPCR chimerism detection values (R2 = 0.9974). The R2 of the dPCR results was higher than STR when the theoretical chimerism rate of the single recipient was ≤ 5%. Clinical verification in 44 HSCT patients suggested a close correlation between STR and dPCR on most occasions, (mean difference, 0.68%) and differences between the two techniques in some cases.

Conclusion: Our new system has great repeatability and sensitivity, especially in detecting micro-chimerism and in dual donor samples. It is expected to show good applicability in Chinese transplant patients. Selecting dPCR/STR testing according to each individual's chimerism status facilitates sensitive and accurate analysis, effective early treatment interventions, and relapse monitoring in clinical settings.