Next-generation sequencing as input for chemometrics in differential sensing routines

Area of Science:

• Biotechnology and Analytical Chemistry
• Nucleic Acid Aptamer Technology
• Biosensing and Diagnostics

Background:

• Traditional differential sensing (DS) relies on spatial arrays and optical signals for multivariate data analysis.
• Existing methods face limitations in classifying complex mixtures and individual analytes without spatial constraints.
• A need exists for adaptable sensing platforms capable of analyzing complex samples without physical receptor arrangements.

Purpose of the Study:

• To develop and demonstrate a novel differential sensing approach utilizing nucleic acid sequences as multivariate data.
• To eliminate the requirement for spatial receptor arrays in differential sensing applications.
• To showcase the applicability of this new method in analyzing complex biological samples, such as differentiating cell lines.

Main Methods:

• Employed selected nucleic acid aptamers as semi-specific receptors.
• Utilized Next-Generation DNA sequencing to generate high-throughput sequence count data.
• Applied principal component analysis (PCA) for multivariate data analysis and visualization of score plots.

Main Results:

• Successfully generated high-dimensionality score plots without spatial arrays.
• Identified cross-reactivity between aptamers through PCA loading plot analysis.
• Demonstrated the method's efficacy using cell line differentiation as a test application.

Conclusions:

• The developed nucleic acid-based differential sensing technique offers a powerful alternative to traditional methods.
• This approach is adaptable for analyzing complex and subtly different analytes where nucleic acid receptors are available.
• The method holds promise for diverse applications in chemical and biological sensing.