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Dual-modality Molecular Cartography: Integrating Multiplex mRNA Detection with Protein Imaging Mass Cytometry

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Multiplexed Integrin Detection and Cancer Cell Classification Using Multicolor Gap-Enhanced Gold Nanorods and Machine

Suprava Shah¹, Reed Youngerman¹, Alberto Luis Rodriguez-Nieves¹

¹Department of Chemistry, The University of Memphis, Memphis, TN 38152, USA.

Nanomaterials (Basel, Switzerland)

|November 26, 2025

Summary

This study introduces a novel five-plex detection platform using surface-enhanced Raman scattering (SERS) and gold nanorods for multiplexed integrin detection. This method accurately classifies breast cancer cells and has potential for detecting circulating tumor cells.

Keywords:

SERS breast cancer gap-enhanced gold nanorod integrin machine learning multiplexed detection

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Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing

Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing

Published on: September 25, 2011

Area of Science:

Biomedical Engineering
Nanotechnology
Cancer Research

Background:

Integrins are key cell-surface receptors in tumor progression, invasion, and metastasis.
Accurate classification of heterogeneous cancer cell subtypes is crucial for diagnosis and therapy.
Multiplexed detection of integrins at the single-cell level offers comprehensive tumor cell profiling.

Purpose of the Study:

To develop a five-plex detection platform for multiplexed integrin profiling and cancer cell classification.
To leverage surface-enhanced Raman scattering (SERS) with gap-enhanced gold nanorods (GENRs) for sensitive and specific detection.
To utilize advanced computational analysis for accurate quantification and classification of cancer cells based on integrin expression.

Main Methods:

Synthesis of GENRs functionalized with five distinct Raman nanotags targeting specific integrin subtypes.
Simultaneous labeling of single cancer cells with five-color SERS nanotags.
Analysis of SERS signals using classical least squares regression for integrin quantification and random forest classification for cell type identification.

Main Results:

Achieved highly accurate (99.9%) classification of three different breast cancer cell lines based on their integrin profiles.
Demonstrated reliable deconvolution and quantification of five different integrin monomers.
Successfully tested the feasibility of detecting mixed breast cancer cells spiked in peripheral blood mononuclear cells (PBMCs).

Conclusions:

The developed platform significantly advances cancer diagnostics through accurate integrin-based cell profiling and classification.
Multiplexed integrin detection using SERS technology shows potential for improved cancer subtype characterization.
This approach supports personalized diagnostics and the development of targeted therapeutic strategies.