DeltaPCA: A Statistically Robust Method for Detecting Protein Analyte Binding to Aptamer-Functionalised Nanoparticles using Surface-Enhanced Raman Spectroscopy

13 September 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


In this work, we introduce a novel joint experimental design and computational analysis procedure to reliably and reproducibly quantify protein analyte binding to DNA aptamer-functionalised silver nanoparticles using slippery surface-enhanced Raman spectroscopy. We employ an indirect detection approach, based upon monitoring spectral changes in the covalent bond-stretching region as intermolecular bonds are formed between the surface-immobilized probe biomolecule and its target analyte. Sample variability is minimized by preparing aptamer-only and aptamer-plus-analyte samples under the same conditions, and then analysing difference spectra. To account for technical variability, multiple spectra are recorded from the same sample. Our new DeltaPCA analysis procedure takes into account technical variability within each spectral data set while also extracting statistically robust difference spectra between data sets. Proof of principle experiments using thiolated aptamers to detect CoV-SARS-2 spike protein reveal that analyte binding is mediated through the formation of N-H...X and C-H...X hydrogen bonds between the aptamer (H-bond donor) and protein (H-bond acceptor). Our computational analysis code can be freely downloaded from


Raman spectroscopy
Biomedical diagnostic
Data processing
Principal component analysis
Technical variability
Sample variability
Open-source software
DNA aptamer
Silver nanoparticle

Supplementary materials

Justification of calibration curve analysis procedure
Short derivation that shows why double-logarithm linear regression is an appropriate analysis procedure for constructing SERS calibration curves at low analyse concentrations, starting from the generalised Langmuir binding model.


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