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. 2023 Jan 19:14:1023116.
doi: 10.3389/fimmu.2023.1023116. eCollection 2023.

New methods for the quantification of mixed chimerism in transplantation

Christophe Picard  1   2 , Coralie Frassati  1 , Nicem Cherouat  1 , Sandrine Maioli  1 , Philippe Moskovtchenko  3 , Mathilde Cherel  4 , Jacques Chiaroni  1   2 , Pascal Pedini  1   2
Affiliations

New methods for the quantification of mixed chimerism in transplantation

Christophe Picard et al. Front Immunol. .

Abstract

Background: Quantification of chimerism showing the proportion of the donor in a recipient is essential for the follow-up of hematopoietic stem cell transplantation but can also be useful to document an immune tolerance situation after solid organ transplantation. Historically, chimerism has been quantified from genomic DNA, but with technological advances, chimerism from donor-derived cell-free DNA seems particularly relevant in solid organ transplantation.

Methods: The reference method was until recently the short tandem repeat technique, but new innovative techniques as digital PCR (dPCR) and NGS, have revolutionized the quantification of chimerism, such as the so-called microchimerism analysis. After a short review of chimerism methods, a comparison of chimerism quantification data for two new digital PCR systems (QIAcuity™ dPCR (Qiagen®) and QuantStudio Absolute Q (ThermoFisher®) and two NGS-based chimerism quantification methods (AlloSeq HCT™ (CareDx®) and NGStrack™ (GenDX®)) was performed.

Results: These new methods were correlated and concordant to routinely methods (r²=0.9978 and r²=0.9974 for dPCR methods, r²=0.9978 and r²=0.9988 for NGS methods), and had similar high performance (sensitivity, reproductibility, linearity).

Conclusion: Finally, the choice of the innovative method of chimerism within the laboratory does not depend on the analytical performances because they are similar but mainly on the amount of activity and the access to instruments and computer services.

Keywords: NGS (next generation sequencing); cfDNA (cell-free DNA); chimerism; dPCR (digital PCR); qPCR (quantitative PCR).

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Correlation of chimerism results between Absolute Q, Qiacuity dPCR and ddPCR method (reference method). ACS. Artificial Chimeric Samples; EQA. External Quality Assessment.
Figure 2
Figure 2
Concordance of the chimerism results between Absolute Q and ddPCR (A) and between Qiacuity and ddPCR (B) using the Bland-Altman plot. The Bland-Altmann plot includes the ± 2 standard deviation value (dotted lines) and ±3 standard deviation value (solid lines). ACS. Artificial Chimeric Samples; EQA. External Quality Assessment.
Figure 3
Figure 3
Reproducibility of chimerism results from Absolute Q and Qiacuity dPCR. Each chimerism percentage (50%, 10%, 5%, 0.5%, 0.1%) was tested on at least three different experiments to calculate the coefficient of variation of each method.
Figure 4
Figure 4
Correlation of chimerism results between AlloSeq HCT, NGStrack and ddPCR methods. ACS. Artificial Chimeric Samples; EQA. External Quality Assessment.
Figure 5
Figure 5
Concordance of the chimerism results between AlloSeq HCT and ddPCR (A) and between NGStrack and ddPCR (B) using the Bland-Altman plot. The Bland-Altmann plot includes the ± 2 standard deviation value (dotted lines) and ±3 standard deviation value (solid lines). ACS. Artificial Chimeric Samples; EQA. External Quality Assessment.
Figure 6
Figure 6
Reproducibility of chimerism results from AlloSeq HCT and NGStrack. Each chimerism percentage (50%, 10%, 5%, 0.5%) was tested on at least three different experiments to calculate the coefficient of variation of each method.
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