Oligonucleotide selective detection by levitated optomechanics

TL;DR

The paper investigates selective detection of oligonucleotide coatings on silica nanoparticles via levitated optomechanics. It functionalizes 100 nm silica nanoparticles with ZnCl$_2$-bridged 25A or 25T oligonucleotides, traps them in vacuum with a 1550 nm laser, and analyzes motion spectra through Lorentzian fits, UMAP clustering, and random forest classification, supported by a polarizability-to-mass model. The results predict measurable trap-frequency shifts per oligo layer and even per single nucleotide, suggesting potential for base-level discrimination, though TEM could not visually differentiate groups; the approach offers a non-destructive optical pathway toward DNA sequence-related detection with implications for diagnostics and sequencing challenges. Future work could include SEM-EDS for elemental confirmation and exploring duplex DNA effects to broaden applicability.

Abstract

This study examines the detection of oligonucleotide-specific signals in sensitive optomechanical experiments. Silica nanoparticles were functionalized using ZnCl$_2$ and 25-mers of single-stranded deoxyadenosine and deoxythymidine monophosphate which were optically trapped by a 1550 nm wavelength laser in vacuum. In the optical trap, silica nanoparticles behave as harmonic oscillators, and their oscillation frequency and amplitude can be precisely detected by optical interferometry. The data was compared across particle types, revealing differences in frequency, width and amplitude of peaks with respect to motion of the silica nanoparticles which can be explained by a theoretical model. Data obtained from this platform was analyzed by fitting Lorentzian curves to the spectra. Dimensionality reduction detected differences between the functionalized and non-functionalized silica nanoparticles. Random forest modeling provided further evidence that the fitted data were different between the groups. Transmission electron microscopy was carried out, but did not reveal any visual differences between the particle types.

Figures (9)

• Figure 1: Functionalization: The process of functionalizing silica nanoparticles with 25-mer deoxyadenosine monophosphate (25A) or 25-mer deoxythymidine monophosphate (25T) oligonucleotides, water and ZnCl$_2$ solution. Eppendorf tube images are provided by https://www.labicons.net.
• Figure 2: PSD plots of groups of selected particle types with frequency peaks f$_1$, f$_2$ and f$_3$ displayed. (a) Plots of the f$_1$, f$_2$ and f$_3$ peaks for one particle of each type: 25A silica nanoparticle at a 1000 $\mu$M ZnCl$_2$ concentration (red), 25T silica nanoparticle at a 1000 $\mu$M ZnCl$_2$ concentration (blue) and for a standard silica nanoparticle (grey). (b) Plots of the f$_1$, f$_2$ and f$_3$ peaks for one particle of each type: 25T silica nanoparticle at a 1000 $\mu$M ZnCl$_2$ concentration (blue), 25T silica nanoparticle at a 100 $\mu$M ZnCl$_2$ concentration (turquoise), 25T silica nanoparticle at a 500 $\mu$M ZnCl$_2$ concentration (purple) and 25T silica nanoparticle at a 750 $\mu$M ZnCl$_2$ concentration (orange).
• Figure 3: 2-Dimensional UMAP analysis of optically trapped silica nanoparticles. (a) This UMAP scatter plot depicts the differences between the trapped 25A silica nanoparticles and 25T silica nanoparticles plotted alongside the standard silica nanoparticles. Both 25A silica nanoparticles and 25T silica nanoparticles use a 1000 $\mu$M concentration of ZnCl$_2$. (b) This UMAP scatter plot demonstrates the similarities and differences between varying the concentrations of ZnCl$_2$ on the binding of 25T to the silica nanoparticle surface. Both (a) and (b) use the following parameters of number of nearest neighbors = 50 and minimum distance = 0.0 to observe the global difference between particle types.
• Figure 4: Random forest results comparing two groups of silica nanoparticles. (a) Random forest model accuracy for the 25A and 25T silica nanoparticles at 1000 $\mu$M ZnCl$_2$ and standard silica nanoparticles data over 300 MCCV iterations. (b) Random forest model accuracy for the 25T silica nanoparticles at different ZnCl$_2$ concentrations data over 300 MCCV iterations. (c) Mean feature importances of both datasets over 300 MCCV iterations.
• Figure 5: TEM images of silica nanoparticles. (a) 25T 100 $\mu$M ZnCl$_2$ silica nanoparticles. (b) 25T 500 $\mu$M ZnCl$_2$ silica nanoparticles. (c) 25T 1000 $\mu$M ZnCl$_2$ silica nanoparticles. (d) Standard silica nanoparticle. (e) 25A 1000 $\mu$M ZnCl$_2$ silica nanoparticles. (f) 25T 750 $\mu$M ZnCl$_2$ silica nanoparticle. a – c are at 100,000 $\times$ magnification. d – f are at 600,000 $\times$ magnification.