Assessment of unmixing approaches for the quantitation of SERS nanoparticles in highly multiplexed spectral images

Abstract

Surface‐enhanced Raman scattering nanoparticles (SERS NPs) offer powerful optical contrast features for imaging assays. Their gold core enhances the inelastic scattering cross section, allowing highly sensitive and rapid detection, and their characteristic sets of narrow spectral bands give them unsurpassed multiplexing capabilities. Multiplexed hyperspectral images are commonly unmixed using a compensation matrix of reference spectra to produce quantitative image channels illustrating the distribution of each material. It is these unmixed channels that are fit for interpretation from assays utilizing SERS NP contrast agents. Some factors that may impact SERS NP quantitative and dynamic range capabilities may include endogenous background heterogeneity, the ability of unmixing algorithms to account for signal variances, and linear system conditioning imposed by contrast agent signals. We report on hyperspectral Raman imaging of mixtures of SERS NPs from an expanded library of contrast agents. We study increasing plexity and varying degrees of system conditioning as inputs to a diverse set of classical, non‐negatively constrained, and regularized regression algorithms to investigate which signal features and unmixing methods deliver the most promising quantitation performance with the least error. Raman imaging of SERS NP mixtures is performed on controlled substrates and representative biological specimens, and experimental results are compared against ground truth data. We evaluate spectral fitting fidelity, quantitation, and specificity correlations with system conditioning. Spectral unmixing with a regularized hybrid of least squares regression with principal component analysis (HLP) algorithm approximated spectra with 3.5× better fitting fidelity and 3× better quantitation robustness with tissue background compared with simpler unmixing routines.

A Raman topography imaging method toward assisting surgical tumor resection

“…Quantitation capacity is important and advantageous in a molecular imaging modality because it enables contrast agent abundances and therefore biological expression levels to be accurately analyzed and objectively compared. We show here and in previous work that protein expression levels by cells can be robustly assessed with quantitative SERS NP imaging 34,59 . With SERS NP flavors each designed with antibodies to target a multitude of biomarkers and a flavor conjugated to an isotype control antibody, various levels of relevant proteins could be evaluated and specifically localized across a tissue specimen's surface.…”

A Systematic Approach toward Enabling Maximal Targeting Efficiency of Cell Surface Proteins with Actively Targeted SERS Nanoparticles

“…After pioneering applications of SERS NPs for molecular imaging were introduced in vivo by Gambhir’s group, , several research teams focused their efforts toward (1) developing new Raman imaging tools; (2) unraveling the multiplexing potential of SERS NPs; ,− (3) increasing their sensitivity down to sub-fM limits of detection (LODs); (4) increasing penetration depth of Raman imaging; − (5) improving speed of Raman imaging in conjunction with SERS NPs; (6) optimizing spectral unmixing algorithms; and (7) adding multiple imaging modalities. − Furthermore, SERS NPs have demonstrated their potential in detection of various important biomarkers such as carbonic anhydrase IX (CA9), cluster of differentiation 24 (CD24), CD44, , CD47, ,, epidermal growth factor receptor (EGFR), − ,,− epithelial cellular adhesion molecule (EpCAM), estrogen receptor (ER), human epididymis protein 4 (HE4), human epidermal growth factor receptor 2 (HER2), − ,, programmed death-ligand 1 (PD-L1), , and progesterone receptor (PR) . However, most studies have not comprehensively analyzed the distinctive attributes of the SERS NPs for achieving a high biotargeting specificity.…”