SERSTEM: An app for the statistical analysis of correlative SERS and TEM imaging and evaluation of SERS tags performance

Raman spectroscopy is becoming increasingly popular as an in vitro bioimaging technique, when coupled with plasmonic substrates such as gold nanoparticles (AuNPs). Plasmonic AuNPs not only display excellent biocompatibility but can also induce the surface-enhanced Raman scattering (SERS) effect, which can be exploited for cell labeling, as an interesting alternative to fluorescence-based techniques. SERS bioimaging requires the use of so-called SERS tags or SERS-encoded AuNPs. A remaining difficulty toward the general implementation of this method is the difficulty to correlate the SERS signal (spectral intensity) with the number of SERS tags. Therefore, a general correlation method, suitable for arbitrary AuNP morphologies and Raman-active molecules (Raman reporters or RaRs), should largely improve the quantitative character of SERS as an imaging technique. We propose a protocol, with an associated app (SERSTEM), which enables the user to determine the average SERS intensity per nanoparticle from transmission electron microscopy (TEM) and SERS data. As a proof of concept, we demonstrated the method for Au nanostars and nanorods, carrying four different RaRs, and implemented the SERSTEM app, which is publicly available from an open-source platform.

Automated summary

Raman spectroscopy is increasingly popular for in vitro bioimaging when coupled with plasmonic substrates like gold nanoparticles. The SERS effect induced by plasmonic AuNPs offers an interesting alternative to fluorescence-based techniques for cell labeling. Difficulty in correlating SERS signal with the number of tags remains a challenge, necessitating a general correlation method for improved quantitative character of SERS as an imaging technique.