Dataset Information

Dataset of human platelets in healthy and individuals with cardiovascular pathology obtained by surface-enhanced Raman spectroscopy.

ABSTRACT: This data article contains Raman experimental data, obtained with Centaur U Raman spectrometer (Russia), which can be used for rapid and early structure changes and biomarkers identification in individuals with cardiovascular decease (CVD) pathology in vitro. The data include analyzed Surface-Enhanced Raman Scattering (SERS) spectra of human platelets taken from healthy individuals and individuals with cardiovascular pathology. Data can provide information about characteristic maxima of different cell components and its changes in platelets.

SUBMITTER: Zyubin A

PROVIDER: S-EPMC6992932 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Dataset of human platelets in healthy and individuals with cardiovascular pathology obtained by surface-enhanced Raman spectroscopy.

Zyubin Andrey A Rafalskiy Vladimir V Tcibulnikova Anna A Matveeva Karina K Moiseeva Ekaterina E Tsapkova Alina A Samusev Ilia I Bryukhanov Valery V Demin Maksim M

Data in brief 20200115

This data article contains Raman experimental data, obtained with Centaur U Raman spectrometer (Russia), which can be used for rapid and early structure changes and biomarkers identification in individuals with cardiovascular decease (CVD) pathology <i>in vitro</i>. The data include analyzed Surface-Enhanced Raman Scattering (SERS) spectra of human platelets taken from healthy individuals and individuals with cardiovascular pathology. Data can provide information about characteristic maxima of d ...[more]

PMID: 32016148

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Project description:Pesticides pose a great threat to human health and their rapid detection has become an urgent public safety issue engaging the scientific community to search for fast and reliable detection techniques. In this context, Surface Enhanced Raman Spectroscopy (SERS) has emerged as a valuable detection and analysis tool due to its high sensitivity and selectivity, proving its suitability for the food industry and environmental monitoring applications. Here, we report on the fabrication of colloidal silver nanoparticle (AgNP) films by convective self-assembly (CSA) on solid planar substrate and their use for the SERS analyses of two types of pesticides, the fungicide thiabendazole (TBZ) and the insecticide α-endosulfan (α-ES). Electron microscopy shows that these nanoparticle films are dense, highly compact, and uniform across several mm2 areas. The SERS efficiency of the fabricated AgNP films is evaluated using a well-known Raman probe, p-aminothiophenol, for multiple excitation laser lines (532 nm, 633 nm, and 785 nm). The films exhibit the largest SERS enhancement factors for 785 nm excitation, reaching values larger than 105. Thiabendazole could be readily adsorbed on the AgNPs without any sample surface functionalization and detected down to 10-6 M, reaching the sub-ppm range. Endosulfan, a challenging analyte with poor affinity to metal surfaces, was captured near the metal surface by using self-assembled alkane thiol monolayers (hexanethiol and octanethiol), as demonstrated by the thorough vibrational band analysis, and supported by density functional theory (DFT) calculations. In addition, principal component analysis (PCA) based on SERS spectra offers significant leverage in discrimination of the molecules anchored onto the metallic nanostructured surface. This present study demonstrates the utility of self-assembled colloidal nanoparticle films as SERS substrates for a broad range of analytes (para-aminothiophenol, thiabendazole, α-endosulfan, and alkanethiols) and contributes to the development of SERS-based sensors for pesticides detection, identification and monitoring.

Project description:Characterizing complex biofluids using surface-enhanced Raman spectroscopy (SERS) coupled with machine learning (ML) has been proposed as a powerful tool for point-of-care detection of clinical disease. ML is well-suited to categorizing otherwise uninterpretable, patient-derived SERS spectra that contain a multitude of low concentration, disease-specific molecular biomarkers among a dense spectral background of biological molecules. However, ML can generate false, non-generalizable models when data sets used for model training are inadequate. It is thus critical to determine how different SERS experimental methodologies and workflow parameters can potentially impact ML disease classification of clinical samples. In this study, a label-free, broadband, Ag nanoparticle-based SERS platform was coupled with ML to assess simulated clinical samples for cardiovascular disease (CVD), containing randomized combinations of five key CVD biomarkers at clinically relevant concentrations in serum. Raman spectra obtained at 532, 633, and 785 nm from up to 300 unique samples were classified into physiological and pathological categories using two standard ML models. Label-free SERS and ML could correctly classify randomized CVD samples with high accuracies of up to 90.0% at 532 nm using as few as 200 training samples. Spectra obtained at 532 nm produced the highest accuracies with no significant increase achieved using multiwavelength SERS. Sample preparation and measurement methodologies (e.g., different SERS substrate lots, sample volumes, sample sizes, and known variations in randomization and experimental handling) were shown to strongly influence the ML classification and could artificially increase classification accuracies by as much as 27%. This detailed investigation into the proper application of ML techniques for CVD classification can lead to improved data set acquisition required for the SERS community, such that ML on labeled and robust SERS data sets can be practically applied for future point-of-care testing in patients.

Dataset Information

Dataset of human platelets in healthy and individuals with cardiovascular pathology obtained by surface-enhanced Raman spectroscopy.

Publications

Dataset of human platelets in healthy and individuals with cardiovascular pathology obtained by surface-enhanced Raman spectroscopy.

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