A new model for quantifying nanoparticle concentration using SERS supported by multi-modal mass spectrometry

Leventi, Aristea Anna and Billimoria, Kharmen and Bartczak, Dorota and Laing, Stacey and Goenaga-Infante, Heidi and Faulds, Karen and Graham, Duncan (2023) A new model for quantifying nanoparticle concentration using SERS supported by multi-modal mass spectrometry. Analytical Chemistry, 95 (5). pp. 2757-2764. ISSN 0003-2700 (https://doi.org/10.1021/acs.analchem.2c03779)

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Abstract

Surface-enhanced Raman scattering (SERS) is widely explored for the elucidation of underlying mechanisms behind biological processes. However, the capability of absolute quantitation of the number of nanoparticles from the SERS response remains a challenge. Here, we show for the first time the development of a new 2D quantitation model to allow calibration of the SERS response against the absolute concentration of SERS nanotags, as characterized by single particle inductively coupled plasma mass spectrometry (spICP-MS). A novel printing approach was adopted to prepare gelatin-based calibration standards containing the SERS nanotags, which consisted of gold nanoparticles and the Raman reporter 1,2-bis(4-pyridyl)ethylene. spICP-MS was used to characterize the Au mass concentration and particle number concentration of the SERS nanotags. Results from laser ablation inductively coupled plasma time-of-flight mass spectrometry imaging at a spatial resolution of 5 μm demonstrated a homogeneous distribution of the nanotags (between-line relative standard deviation < 14%) and a linear response of 197Au with increasing nanotag concentration (R 2 = 0.99634) in the printed gelatin standards. The calibration standards were analyzed by SERS mapping, and different data processing approaches were evaluated. The reported calibration model was based on an “active-area” approach, classifying the pixels mapped as “active” or “inactive” and calibrating the SERS response against the total Au concentration and the particle number concentration, as characterized by spICP-MS. This novel calibration model demonstrates the potential for quantitative SERS imaging, with the capability of correlating the nanoparticle concentration to biological responses to further understand the underlying mechanisms of disease models.

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