SERS-Based Quantification of PSMA in Tissue Microarrays Allows Effective Stratification of Patients with Prostate Cancer

Manjari Bhamidipati, Geuntaek Lee, Isaac Kim, Laura Fabris

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Prostate specific membrane antigen (PSMA), a type II membrane protein, is an attractive biomarker that has been validated clinically for the diagnosis of prostate cancer. In this study, we developed surface-enhanced Raman scattering (SERS) nanoprobes for PSMA detection and quantification at the single-cell level on prostate cancer cells. The cells were targeted employing SERS nanoprobes that consisted of gold nanostars functionalized with PSMA aptamer molecules. We were able to quantify picomolar concentrations of soluble PSMA protein and used the resulting calibration curve to estimate the expression of PSMA on the surface of the prostate cancer cell, LNCaP, at the single-cell level. Importantly, we employed these SERS tags to stratify prostate cancer patients by assessing PSMA expression in tissues contained in a prostate tissue microarray. The stratification results clearly correlated PSMA expression to recommended therapy groups, rendering the described method as an effective tool to aid in designing personalized therapeutic protocols. Benchmarking detection sensitivity against immunofluorescence staining and comparing stratification results obtained with the two methods allowed us to validate our novel approach against standard practices. On the basis of these results, we confirm the validity of PSMA as an effective biomarker for prostate cancer patient evaluation and propose SERS-based diagnostic techniques as integrative methods for the assessment of disease stage and the identification of effective therapeutic protocols.

Original languageEnglish (US)
Pages (from-to)16784-16794
Number of pages11
JournalACS Omega
Issue number12
StatePublished - Dec 6 2018

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Chemical Engineering(all)


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