Fabricating a Raman spectrometer using an optical pickup unit and pulsed power

Direct Glycan Analysis of Biological Samples and Intact Glycoproteins by Integrating Machine Learning-Driven Surface-Enhanced Raman Scattering and Boronic Acid Arrays

Frequent monitoring of glycan patterns is a critical step in studying glycan-mediated cellular processes. However, the current glycan analysis tools are resource-intensive and less suitable for routine use in standard laboratories. We developed a novel glycan detection platform by integrating surface-enhanced Raman spectroscopy (SERS), boronic acid (BA) receptors, and machine learning tools. This sensor monitors the molecular fingerprint spectra of BA binding to cis-diol-containing glycans. Different types of BA receptors could yield different stereoselective reactions toward different glycans and exhibit unique vibrational spectra. By integration of the Raman spectra collected from different BA receptors, the structural information can be enriched, eventually improving the accuracy of glycan classification and quantification. Here, we established a SERS-based sensor incorporating multiple different BA receptors. This sensing platform could directly analyze the biological samples, including whole milk and intact glycoproteins (fetuin and asialofetuin), without tedious glycan release and purification steps. The results demonstrate the platform's ability to classify milk oligosaccharides with remarkable classification accuracy, despite the presence of other non-glycan constituents in the background. This sensor could also directly quantify sialylation levels of a fetuin/asialofetuin mixture without glycan release procedures. Moreover, by selecting appropriate BA receptors, the sensor exhibits an excellent performance of differentiating between α2,3 and α2,6 linkages of sialic acids. This low-cost, rapid, and highly accessible sensor will provide the scientific community with an invaluable tool for routine glycan screening in standard laboratories.

A design method for direct vision coaxial linear dispersion spectrometers

A spectrometer design method based on the prism-prism-grating (PPG) dispersion module is proposed in this paper to correct the serious nonlinear dispersion that prism and grating spectrometers and other dispersive spectrometers suffer from. First, we determine the criteria for selecting the optical materials of the PPG module by analyzing the dispersion characteristics of prisms and gratings. Second, a loop traversal algorithm is used to optimize the system structure parameters after selecting optical materials. Next, the direct vision coaxial condition of the PPG module is derived according to basic optical principles and the geometrical relationship between optical elements. Then, the dispersion equation of the PPG module is used to establish the spectral linearity index of the system. Finally, combined with the design index, the structural parameters of the PPG module to meet the linear dispersion requirements are determined. A direct vision coaxial linear dispersion spectrometer is designed and realized under the condition that the working band is 400-990 nm, the deviation angle and offset of the emitted ray with a central wavelength of 695 nm with respect to the optical axis are 0, and the dispersion angle is not less than 15°. The results simulated by ZEMAX show that the actual simulation results are consistent with the theoretical calculation results, the spectral resolution of the spectrometer is less than 1.5 nm, and the spectral smile and keystone are less than 3.89% pixels. In the discussion section, the influences of the dispersion ability of optical materials and the incident angles of prisms and gratings on the spectral dispersion linearity of the PPG module are analyzed and studied. The universality of the spectrometer design method developed in this paper is discussed, and its universality is simulated and verified in the 1000-1600 nm and 1600-2200 nm bands. In addition, some advantages compared with other dispersion structures are analyzed.

Optical Detection of CoV-SARS-2 Viral Proteins to Sub-Picomolar Concentrations

The emergence of a new strain of coronavirus in late 2019, SARS-CoV-2, led to a global pandemic in 2020. This may have been preventable if large scale, rapid diagnosis of active cases had been possible, and this has highlighted the need for more effective and efficient ways of detecting and managing viral infections. In this work, we investigate three different optical techniques for quantifying the binding of recombinant SARS-CoV-2 spike protein to surface-immobilized oligonucleotide aptamers. Biolayer interferometry is a relatively cheap, robust, and rapid method that only requires very small sample volumes. However, its detection limit of 250 nM means that it is not sensitive enough to detect antigen proteins at physiologically relevant levels (sub-pM). Surface plasmon resonance is a more sensitive technique but requires larger sample volumes, takes longer, requires more expensive instrumentation, and only reduces the detection limit to 5 nM. Surface-enhanced Raman spectroscopy is far more sensitive, enabling detection of spike protein to sub-picomolar concentrations. Control experiments performed using scrambled aptamers and using bovine serum albumin as an analyte show that this apta-sensing approach is both sensitive and selective, with no appreciable response observed for any controls. Overall, these proof-of-principle results demonstrate that SERS-based aptasensors hold great promise for development into rapid, point-of-use antigen detection systems, enabling mass testing without any need for reagents or laboratory expertise and equipment.