Quantification of total nucleic acids from unknown sources (RNA eq)

Importance of the topic

The precise quantification of total nucleic acids from diverse biological sources is critical for molecular biology workflows, quality control in nucleic acid extractions, and downstream applications such as sequencing, PCR, and cloning. Unresolved impurities and turbidity can lead to erroneous concentration readings, impacting experimental reproducibility and data reliability.

Goals and overview

This application note presents the implementation of the Nucleic Acids (RNA equivalent) Unmix application on Lunatic systems. Its objective is to isolate and quantify the total nucleic acid fraction, expressed in RNA equivalents, from an unknown mixture of nucleic acids and co-absorbing substances by analyzing UV/Vis spectral data.

Methodology

The Unmix algorithm deconvolutes the measured UV/Vis absorption spectrum into three profiles: nucleic acids, impurities, and background turbidity. The nucleic acid profile leverages the A260 absorbance peak and an RNA concentration factor to calculate concentration in ng/µL RNA equivalent. Impurities are reported in OD260 or OD280 values according to specific thresholds. Background turbidity is subtracted from the raw spectrum to yield the content spectrum used for quantification. A residual fit quality parameter (RRSE) indicates the fraction of unassigned spectral data; values above 2.5% trigger warnings and suppress detailed impurity or background reporting when combined with low absorbance readings.

Used instrumentation

• Lunatic spectrophotometer system with Unmix application interface.
• Little Lunatic portable spectrophotometer.

Main results and discussion

Applying the Unmix application enables rapid on-screen display of spectral profiles and calculated values for total nucleic acids, impurities, and background. The method distinguishes a wide range of nucleic acid types, including total RNA, degraded RNA, and DNA with varying GC content. Impurities such as phenol, salts, and proteins are quantified against predefined thresholds. The residual fit metric ensures data quality by flagging samples with excessive turbidity or unknown absorbers.

Benefits and practical applications

• No additional user input beyond blank selection is required, simplifying routine workflows.
• High throughput spectral analysis with automated deconvolution of complex mixtures.
• Integrated reporting in multiple formats (HTML, XML, TXT, CSV, XLSX, PDF) for flexible data management.

Future trends and potential applications

Advancements may include machine learning-driven spectral classification to expand impurity detection, integration with laboratory information management systems for automated data logging, and adaptation to microvolume or multiplexed formats. Further development could enable targeted discrimination of specific nucleic acid species and real-time quality feedback during sample preparation.

Conclusion

The Nucleic Acids (RNA equivalent) Unmix application on Lunatic platforms offers a robust, user-friendly solution for accurate quantification of total nucleic acids from unknown sources. By combining spectral unmixing, quality assessment, and customizable reporting, the method enhances reliability in nucleic acid analysis and supports diverse research and quality control needs.