Profiling the heterogeneous landscape of cell types and biomolecules is rapidly being adopted to address current imperative research questions. Precision medicine seeks advancements in molecular spatial profiling techniques with highly multiplexed imaging capabilities and sub-cellular resolution, which remains an extremely complex task. Surface-enhanced Raman spectroscopy (SERS) imaging offers new promise through the utilization of nanoparticle-based contrast agents that exhibit narrow spectral features and molecular specificity.
Herein, we report the first demonstration of simultaneously multiplexing 26 different nanoparticles in a single imaging pixel with subcellular resolution.
SERS nanoparticles are powerful optical contrast agents for imaging assays. Their highly specific sets of narrow spectral bands make them well suited for multiplexing applications, and their enhanced inelastic scattering cross sections enable rapid, high content imaging. Multiplexed hyperspectral imaging datasets commonly undergo a spectral unmixing postprocessing step using a compensation matrix of reference spectra to produce quantitative image channels. We perform hyperspectral Raman imaging on mixtures with increasing plexity and varying degrees of linear system conditioning and compare against the ground truth to determine the most robust workflow for quantitative biological SERS imaging.
Negative surgical margins can be difficult to confirm intraoperatively. We propose a workflow of immunostaining and surface imaging of fresh excised tissue using highly sensitive and spectrally separable SERS nanoparticles as the targeted contrast agent. The adaptive focus capabilities of an advanced Raman instrument, combined with our rotational accessory tool for exposing each surface of the stained specimen to the objective lens, enables topographic mapping of the entire excised specimen’s surface. Detailed surface renderings color-encoded according to unmixed SERS nanoparticle abundances show a path forward for high-content, interactive surgical margin assessment.
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