Nano-engineered surface enhanced Raman spectroscopy substrates for probing tissue-material interactions

TL;DR

This paper introduces a nanoengineered SERS substrate made of gold nano-columns on titanium that enables real-time, noninvasive, multiplexed monitoring of tissue-implant interactions, advancing biomedical diagnostics.

Contribution

The study develops a biocompatible, self-sensing implant material with high SERS enhancement for multiplexed, nondestructive tissue-material interaction analysis using machine learning.

Findings

Achieved a SERS enhancement factor of 1.8×10^5.

Demonstrated spatial identification of tissue components.

Confirmed biocompatibility through cytotoxicity assays.

Abstract

Innovation in biomaterials has brought both breakthroughs and new challenges in medicine, as implant materials have become increasingly multifunctional and complex. One of the greatest issues is the difficulty in assessing the temporal and multidimensional dynamics of tissue-implant interactions. Implant biology remains hard to decipher without a noninvasive and multiplexed technique that can accurately monitor real-time biological processes. To address this, we developed a multifunctional, self-sensing implant material composed of gold nano-columns patterned on a titanium surface (AuNC-Ti). This material acts as a nanoengineered surface-enhanced Raman spectroscopy (SERS) substrate that amplifies biological Raman signals at the tissue-implant interface, providing the ability to sense tissue-material interactions in a multiplexed and nondestructive manner. AuNC-Ti SERS substrates were fabricated using oblique angle deposition (OAD) and characterized using scanning electron microscopy (SEM) to show uniform formation of AuNCs ($360 \pm 40$ nm in length and $50 \pm 16$ nm in width). X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and contact angle measurements demonstrated biocompatible surface chemistry with ideal wettability. Biocompatibility was further demonstrated via in vitro cytotoxicity assays on human aortic endothelial cells (HAECs) cultured on AuNC-Ti surfaces. The median SERS enhancement factor (EF) was calculated to be $1.8 \times 10^5$, and spatial identification of reporter molecules and porcine tissue components on AuNC-Ti surfaces was demonstrated using confocal Raman imaging and multivariate analysis. Our approach utilizes unlabeled SERS and machine learning, promising multiplexed characterization of tissue-material interactions and subsequently enabling tissue state determination and non-invasive monitoring of implant-tissue interaction.