Machine learning-assisted single-cell Raman imaging for rapid, sensitive detection and intracellular mapping of carotenoids in plant cell cultures

Abstract

Abstract Key message CRaman imaging combined with a multi-layer perceptron neural network enables non-destructive, label-freeclassifi cation of tobacco BY-2 cells based on carotenoid composition. Abstract Carotenoids are natural tetraterpenoid pigments with important nutritional properties and broad industrial applications. Enhancing their production in plant-based biofactories offers a sustainable alternative to current manufacturing processes. In this work, we developed a label-free, single-cell analytical platform combining Raman imaging with a multi-layer perceptron neural network to classify tobacco BY-2 cells based on their carotenoid content. Carotenoid standards analysis, including astaxanthin, canthaxanthin, and β-carotene, was performed by surface-enhanced Raman scattering using hydrophobic gold nanostars due to the low concentration available. This analysis allowed the assignment of characteristic Raman peaks, specifically at 1160 cm −1 and 1520 cm −1 , of key carotenoids and their identification inside of the cells by Raman imaging. The Raman fingerprints were correlated with carotenoid profiles obtained by HPLC, enabling accurate differentiation between wild-type and transgenic cell lines. In the analyzed transgenic lines, carotenoids accumulated in vesicle-like structures near the nucleus and along the cytoplasmic membrane. This method provides a non-destructive, label-free approach with high classification accuracy and sorting potential based on carotenoid composition, and may be a useful tool for plant synthetic biology and metabolic engineering.

Plant phenotyping relevance

植物細胞内のカロテノイド組成をラマンイメージングと機械学習で非破壊・単細胞レベルに推定する分析プラットフォームを開発しており、植物表現型取得法が中心である。

Code and data availability

The article describes Raman/SERS datasets and an MLP model for carotenoid detection in tobacco BY-2 cells, but provides no public repository, deposit, or authors' URL for the spectral data, images, or analysis code. The Data Availability Statement only states that all data are included in the article and its supplement

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