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. 2025 Sep;17(13):851-863.
doi: 10.1080/17501911.2025.2544530. Epub 2025 Aug 13.

High-throughput assessment of FMR1 and SNRPN methylation-based newborn screening using IsoPure and QIAcube HT systems

Caleb Cartagena  1   2 , Mohammed Alshawsh  1   3 , Minh Q Bui  1   4 , Dinusha Gamage  1 , Rajvi P Thakor  1 , James Pitt  1   2   5 , Ronda F Greaves  1   2   5 , Meg Wall  1   2   5 , Richard Saffery  1   2 , David J Amor  1   2 , David E Godler  1   2   3
Affiliations

High-throughput assessment of FMR1 and SNRPN methylation-based newborn screening using IsoPure and QIAcube HT systems

Caleb Cartagena et al. Epigenomics. 2025 Sep.

Abstract

Aim: This study compared methylation-specific quantitative melt analysis of FMR1 and SNRPN methylation (mDNA) using automated bisulfite conversion by the magnetic-bead-based IsoPure and column-based QIAcube HT systems.

Methods: Two bisulfite conversion methods were assessed on 3.2 mm punches from the same archival blood spots stored at room temperature for >10 years of individuals with FMR1 premutation (n = 20), fragile X syndrome (FXS, n = 20), or chromosome 15 imprinting disorders (n = 50) and freshly made blood spots from 184 newborns from the general population. Performance criteria were: (i) diagnostic sensitivity and specificity for the conditions screened; (ii) reaction failure rate; (iii) variability in mDNA between groups.

Results: Both methods showed 100% sensitivity and specificity for differentiating FXS and individual chromosome 15 imprinting disorders. IsoPure showed reaction failure rates of 0.365% for SNRPN and 0.74% for FMR1 compared to 19.34% and 2.56%, for QIAcube HT, respectively, with most failed reactions originating from archival blood spots. IsoPure showed lower variability in mDNA values in the neurotypical and condition-specific ranges.

Conclusion: The IsoPure system showed superior performance especially on archival samples, with broader applications for screening and diagnostic testing requiring high-throughput mDNA analyses on materials of limited quantity and quality.

Keywords: Angelman syndrome; DNA methylation; FMR1; Prader-Willi syndrome; SNRPN; bisulfite conversion; fragile X syndrome; high-throughput.

Plain language summary

Testing for a change to DNA known as methylation has been used by researchers and in medical practice to identify people affected with different diseases. We compared how well two procedures that employ robotics, work in a lab. We tested a small amount of blood soaked onto absorbent cards to detect this change. Blood on these cards was from people who either had fragile X syndrome or a chromosome 15 imprinting disorder or did not have these. Both procedures worked equally well on freshly made materials in identifying these conditions without making any mistakes. One of the procedures, however, worked better, on cards not recently made. These results provide new opportunities for automated testing of DNA to detect diseases.

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Conflict of interest statement

David E Godler reporting being an inventor on patents related to the technologies described in this publication and being the executive director of EDG Innovations & Consulting, which receives funds from this intellectual property. He has also acted as a paid consultant for Bellberry, Ltd and Actinogen Medical, Pty, Ltd. No other disclosures were reported.

The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Figures

Figure 1.
Figure 1.
Overview of sample preparation and methylation analysis workflow. Sample preparation process and analysis for each newborn blood spot (NBS) and dried blood spot (DBS) sample used in the study. A single 3.2 mm punch from each sample was utilized for each bisulfite conversion method before undergoing methylation specific-quantitative analysis (MS-QMA) of FMR1 and SNRPN promoters.
Figure 2.
Figure 2.
Intergroup comparisons between automated bisulfite conversions using QIAcube HT and IsoPure systems for SNRPN methylation analyses on the same archival dried blood spot (DBS, n = 90) and freshly made newborn blood spot (NBS, n = 184) samples. (A) QIAcube HT and (B) IsoPure bisulfite conversion methods were applied to NBS samples from infants recruited from the general population for de-identified research and DBS samples from individuals confirmed by standard-of-care testing with a chromosome 15 imprinting (C15) disorders (Prader-Willi [PWS], Angelman [AS], maternal Dup15q [mDup15q] or paternal Dup15q [pDUp15q] syndrome), FMR1 premutation (PM), or with a full mutation (FM) affected with fragile X syndrome. Shaded regions (red for QIAcube HT; blue for IsoPure) indicate the negative control reference ranges for the respective methods. The black line represents the highest MR value observed in NBS samples for each method. The arrow in both (A) and (B) highlights the same individual with a mosaic form of AS. Sample size differences reflect reaction failures flagged by Q-MAX software as either ORR or ND2.
Figure 3.
Figure 3.
Intergroup comparisons between automated bisulfite conversions using QIAcube HT and IsoPure systems for FMR1 methylation analyses on the same archival dried blood spot (DBS, n = 90) and freshly made newborn blood spot (NBS, n = 184) samples. (A) QIAcube HT and (B) IsoPure bisulfite conversion methods were applied to NBS samples from infants recruited from the general population for de-identified research (NBS) and DBS samples from individuals confirmed by standard-of-care testing with a chromosome 15 imprinting (C15) disorders (Prader-Willi [PWS], Angelman [AS], maternal Dup15q [mDup15q] or paternal Dup15q [pDUp15q] syndrome), FMR1 premutation (PM), or with full mutation (FM) affected with fragile X syndrome. Shaded regions (red for QIAcube HT; blue for IsoPure) indicate the negative control reference ranges for the respective methods. The black lines represent highest MR values observed in male and female NBS samples for each method. Sample size differences reflect reaction failures flagged by Q-MAX software as either ORR or ND2.
Figure 4.
Figure 4.
Relationships between QIAcube HT and IsoPure systems for SNRPN methylation analyses on the same archival dried blood spot (DBS) and freshly made newborn blood spot (NBS) samples. Outputs from analyses using the IsoPure system are presented on X-axes and QIAcube HT on Y-axes. Comparisons for: (A) all 274 samples analyzed; (B) 90 DBS samples from individuals with conditions screened; (C) 92 NBS samples from the plate with the highest reaction failure rates from 960 NBS samples screened as part of the EpiGNs program using bisulfite converted DNA by the QIAcube HT system; (D) 92 NBS samples from the plate with the lowest reaction failure rates from the first 960 NBS samples screened as part of the EpiGNs program using bisulfite converted DNA by the QIAcube HT system. Note: lines in blue and red respectively depict ‘ORR’ and ‘ND2’ flags by Q-MAX software representing reaction failures in the corresponding system. MR= methylation ratio.
Figure 5.
Figure 5.
Relationships between QIAcube HT and IsoPure systems for FMR1 methylation analyses on the same archival dried blood spot (DBS) and freshly made newborn blood spot (NBS) samples. Outputs from analyses using the IsoPure system are presented on X-axes and QIAcube HT on Y-axes). Comparisons for: (A) all 274 samples analyzed; (B) 90 DBS samples from individuals with conditions screened; (C) 92 NBS samples from plate with the highest reaction failure rate from 960 NBS samples screened as part of the EpiGNs program using bisulfite converted DNA by the QIAcube HT system; (D) 92 NBS samples from the plate with the lowest reaction failure rate from 960 NBS samples screened as part of the EpiGNs program using bisulfite converted DNA by the QIAcube HT system. Note: lines in blue and red represent ‘ORR’ and ‘ND2’ flags by Q-MAX software representing reaction failures for each corresponding system. MR = methylation ratio.
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