Advanced Imaging Integration: Multi-Modal Raman Light Sheet Microscopy Combined with Zero-Shot Learning for Denoising and Super-Resolution

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 21; p. 7083
• Main Authors: Kumari, Pooja, Keck, Shaun, Sohn, Emma, Kern, Johann, Raedle, Matthias
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 03.11.2024; MDPI
• Subjects: B cells; Biomedical engineering; Biomedical research; Cancer; Cell culture; Deep learning; denoising; Design; Drug development; Drug discovery; fluorescence; Human papillomavirus; Hydrogels; Lasers; Light; light sheet; Medical research; Medicine, Experimental; Microscope and microscopy; Microscopy; Optics; Physiology; raman scattering; rayleigh scattering; Spheroids; Squamous cell carcinoma; zero-shot deconvolution networks
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24217083

This study presents an advanced integration of Multi-modal Raman Light Sheet Microscopy with zero-shot learning-based computational methods to significantly enhance the resolution and analysis of complex three-dimensional biological structures, such as 3D cell cultures and spheroids. The Multi-modal Raman Light Sheet Microscopy system incorporates Rayleigh scattering, Raman scattering, and fluorescence detection, enabling comprehensive, marker-free imaging of cellular architecture. These diverse modalities offer detailed spatial and molecular insights into cellular organization and interactions, critical for applications in biomedical research, drug discovery, and histological studies. To improve image quality without altering or introducing new biological information, we apply Zero-Shot Deconvolution Networks (ZS-DeconvNet), a deep-learning-based method that enhances resolution in an unsupervised manner. ZS-DeconvNet significantly refines image clarity and sharpness across multiple microscopy modalities without requiring large, labeled datasets, or introducing artifacts. By combining the strengths of multi-modal light sheet microscopy and ZS-DeconvNet, we achieve improved visualization of subcellular structures, offering clearer and more detailed representations of existing data. This approach holds significant potential for advancing high-resolution imaging in biomedical research and other related fields.