Quantification of mammalian gDNA

Significance of the Topic

The accurate quantification of genomic DNA (gDNA) from mammalian sources is critical for applications in molecular diagnostics, genomics research and quality control in biomanufacturing. By using spectral unmixing of UV/Vis absorbance data, co-absorbing contaminants can be separated from the target dsDNA signal, leading to more reliable concentration measurements.

Objectives and Study Overview

This application note presents the implementation of the DNA mammalian Unmix application on Unchained Labs’ Lunatic and Little Lunatic spectrophotometers. The goal is to isolate the dsDNA component from mixed UV/Vis spectra of various sample types—blood, saliva, tissue extracts and cultured cells—prepared with different extraction chemistries, and to compare results against the fluorescence-based PicoGreen reference assay.

Methodology and Instrumentation

• Unmix Algorithm: Deconvolutes a measured UV/Vis spectrum into three profiles—dsDNA, impurities and background turbidity—using predefined spectral shapes.
• Quantification Approach: The dsDNA concentration is calculated from the A260 peak of the dsDNA profile multiplied by a factor of 50 (ng/µL per A260 unit). If the dsDNA-specific profile cannot be resolved, total nucleic acids are reported using the collective spectrum.
• Instrumentation: Unchained Labs Lunatic and Little Lunatic systems, with ultrapure water blanks, providing rapid UV/Vis spectra in microvolume format.
• Reporting Formats: Output files in HTML, XML, TXT and CSV on both instruments; additional XLSX and PDF on the Lunatic system.

Main Results and Discussion

• Blood Samples (Qiagen QIAsymphony): The Unmix application detected residual buffer salt absorption at 230 nm, which inflated classic A260 readings. Spectral unmixing yielded dsDNA concentrations closely matching PicoGreen assay values, whereas raw A260 overestimated gDNA content.
• Cultured Cell Samples (chemagen Kit): High RNA levels in cell extracts led to overestimation by traditional A260 measurements. Unmix profiling successfully differentiated RNA and dsDNA spectra, delivering more accurate gDNA quantification aligned with PicoGreen results.
• Quality Assessment: The Residual Relative Squared Error (RRSE) metric indicates the fraction of spectrum not explained by the models. Values below 2.5 % denote a high-quality fit; samples above this threshold trigger a warning flag.

Benefits and Practical Applications

• Improved Accuracy: Eliminates spectral interference from salts, phenol, RNA, thiocyanate and turbidity.
• Operational Efficiency: Minimal user input; automated spectral deconvolution streamlines workflows.
• Versatility: Suitable for a wide range of sample types and extraction protocols.
• Flexible Data Integration: Multiple report formats support downstream data analysis and record keeping.

Future Trends and Opportunities

• Extension to Additional Biomolecules: Development of custom spectral libraries for proteins, metabolites or other nucleic acids.
• Lab Informatics Integration: Automated upload of results into Laboratory Information Management Systems (LIMS) for real-time tracking.
• Machine Learning Enhancements: Adaptive algorithms to refine component detection and further reduce residual errors.
• High-Throughput and Miniaturization: Integration into microplate formats and single-cell applications for large-scale screening.

Conclusion

The DNA mammalian Unmix application on Lunatic systems offers a robust and automated solution for dsDNA quantification in mammalian samples. By isolating the dsDNA spectral signature and correcting for co-absorbing impurities, it delivers accuracy comparable to fluorescence reference assays while surpassing the precision of conventional UV/Vis methods.