Bio-inspired rational design of multiscale topographical interfaces: pansy petal replicas as high-fidelity SERS platforms for single-cell analysis

Engineering biointerfaces that provide both robust cell capture and optimal signal enhancement is a central challenge in the development of materials for cellular diagnostics. Conventional top-down fabrication methods are often complex and costly, limiting their widespread application. Here, we introduce a bio-inspired rational design strategy for creating high-performance SERS platforms for single-cell analysis. By developing a quantitative image analysis methodology, we define a surface complexity coefficient, <i>α</i>, which serves as a predictive metric for the cell-adhesion capacity of a given topography. We demonstrate that pansy petal replicas, identified through this strategy, possess a unique multiscale architecture ideal for erythrocyte analysis. These interfaces exhibit a synergistic interplay between high submicron complexity (<i>α</i> > 20) for robust cell immobilization and cell-conformable micron-scale semi-cavities (8-10 μm) that maximize the interaction area with plasmonic Au nanoparticles (∼30 nm). This optimized topography results in a 2- to 7-fold enhancement of SERS signals from individual erythrocytes compared to other floral-templated substrates. This work not only provides a scalable and cost-effective manufacturing route for advanced SERS materials but also establishes a quantitative framework for designing next-generation biointerfaces for a host of diagnostic and biomedical applications.

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Publication Details

Journal
Journal of Materials Chemistry B
Published
2025-01-01
DOI
https://doi.org/10.1039/d5tb01784j
Primary Topic
Biosensors and Analytical Detection
Type
article
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article

Bio-inspired rational design of multiscale topographical interfaces: pansy petal replicas as high-fidelity SERS platforms for single-cell analysis

Sergey Barshutin, M. N. Barshutina, Dmitry I. Yakubovsky, Aleksey V. Arsenin et al.
Journal of Materials Chemistry B
Biosensors and Analytical Detection
article

Bio-inspired rational design of multiscale topographical interfaces: pansy petal replicas as high-fidelity SERS platforms for single-cell analysis

Sergey Barshutin, M. N. Barshutina, Dmitry I. Yakubovsky, Aleksey V. Arsenin, Valentyn S. Volkov, Ilya A. Zavidovskiy, Nadezda A. Brazhe, Sergey M. Novikov, Г. В. Максимов, Zh. V. Bochkova, А. А. Байжуманов, V. R. Solovei
article en

Abstract

Engineering biointerfaces that provide both robust cell capture and optimal signal enhancement is a central challenge in the development of materials for cellular diagnostics. Conventional top-down fabrication methods are often complex and costly, limiting their widespread application. Here, we introduce a bio-inspired rational design strategy for creating high-performance SERS platforms for single-cell analysis. By developing a quantitative image analysis methodology, we define a surface complexity coefficient, <i>α</i>, which serves as a predictive metric for the cell-adhesion capacity of a given topography. We demonstrate that pansy petal replicas, identified through this strategy, possess a unique multiscale architecture ideal for erythrocyte analysis. These interfaces exhibit a synergistic interplay between high submicron complexity (<i>α</i> > 20) for robust cell immobilization and cell-conformable micron-scale semi-cavities (8-10 μm) that maximize the interaction area with plasmonic Au nanoparticles (∼30 nm). This optimized topography results in a 2- to 7-fold enhancement of SERS signals from individual erythrocytes compared to other floral-templated substrates. This work not only provides a scalable and cost-effective manufacturing route for advanced SERS materials but also establishes a quantitative framework for designing next-generation biointerfaces for a host of diagnostic and biomedical applications.

Journal of Materials Chemistry BVol. 13(44)
Lomonosov Moscow State University (RU), MIREA - Russian Technological University (RU)
Openalex Percentile: Top 73%
Biosensors and Analytical Detection
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