Yadav Vikas, Singh Ruchi, Chaturvedi Maya, Siddhanta Soumik, Chaturvedi Rupesh
Department of Chemistry, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.
School of Biotechnology, Jawaharlal Nehru University, New Delhi 110067, India.
Anal Chem. 2025 Jun 24;97(24):12660-12668. doi: 10.1021/acs.analchem.5c01028. Epub 2025 Jun 11.
Advances in virtual staining and spatial omics have revolutionized our ability to explore cellular architecture and molecular composition with unprecedented detail. Virtual staining techniques, which rely on computational algorithms to map molecular or structural features, have emerged as powerful tools to visualize cellular components without the need for physical dyes, thereby preserving sample integrity. Similarly, spatial omics enable the mapping of biomolecules across tissue or cell surfaces, providing spatially resolved insights into biological processes. However, traditional dye-based staining methods, while widely used, come with significant limitations. In this context, Raman spectroscopy offers a robust, label-free alternative for probing molecular composition at a high resolution. We present a novel algorithm that reconstructs super-resolved Raman images by extracting spectral patterns from surrounding pixels, enabling detailed, label-free visualization of cellular structures. By employing Raman spectroscopy in conjunction with chemometric tools such as principal component analysis (PCA), multivariate curve resolution-alternating least squares (MCR-ALS), and artificial neural network (ANN), we performed a quantitative analysis of key biomolecular components, including collagen, lipids, glycogen, and nucleic acids, and classify the different cancer cell lines with an accuracy of nearly 99%. This approach not only enabled the identification of distinct molecular fingerprints across the different cancer types but also provided a powerful tool for understanding the biochemical variations that underlie tumor heterogeneity. This innovative combination of virtual staining, spatial omics, and advanced chemometrics highlights the potential for more accurate diagnostics and personalized treatment strategies in oncology.
虚拟染色和空间组学的进展彻底改变了我们以前所未有的细节探索细胞结构和分子组成的能力。虚拟染色技术依靠计算算法来绘制分子或结构特征，已成为无需物理染料即可可视化细胞成分的强大工具，从而保持样本完整性。同样，空间组学能够绘制跨组织或细胞表面的生物分子图谱，为生物过程提供空间分辨的见解。然而，传统的基于染料的染色方法虽然被广泛使用，但也有显著的局限性。在这种情况下，拉曼光谱提供了一种强大的、无标记的高分辨率探测分子组成的替代方法。我们提出了一种新算法，通过从周围像素提取光谱模式来重建超分辨拉曼图像，实现细胞结构的详细、无标记可视化。通过将拉曼光谱与主成分分析（PCA）、多元曲线分辨交替最小二乘法（MCR-ALS）和人工神经网络（ANN）等化学计量工具结合使用，我们对包括胶原蛋白、脂质、糖原和核酸在内的关键生物分子成分进行了定量分析，并以近99%的准确率对不同癌细胞系进行了分类。这种方法不仅能够识别不同癌症类型的独特分子指纹，还为理解肿瘤异质性背后的生化变化提供了一个强大的工具。虚拟染色、空间组学和先进化学计量学的这种创新结合凸显了在肿瘤学中实现更准确诊断和个性化治疗策略的潜力。
