Multiple Parallel Fusion Network for Predicting Protein Subcellular Localization from Stimulated Raman Scattering (SRS) Microscopy Images in Living Cells

• Published in: International journal of molecular sciences Vol. 23; no. 18; p. 10827
• Main Authors: Wei, Zhihao, Liu, Wu, Yu, Weiyong, Liu, Xi, Yan, Ruiqing, Liu, Qiang, Guo, Qianjin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 16.09.2022; MDPI
• Subjects: Accuracy; Biological activity; Cells (biology); Cellular structure; Deep learning; Drug development; Fluorescence; Fractions; label-free live cell imaging; Localization; Lung cancer; Mean square errors; Methods; Microscopy; multiple parallel fusion network; Neural networks; nonlinear optical microscopy; Performance evaluation; protein subcellular localization; Proteins; Raman spectra; Signal transduction
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms231810827

Stimulated Raman Scattering Microscopy (SRS) is a powerful tool for label-free detailed recognition and investigation of the cellular and subcellular structures of living cells. Determining subcellular protein localization from the cell level of SRS images is one of the basic goals of cell biology, which can not only provide useful clues for their functions and biological processes but also help to determine the priority and select the appropriate target for drug development. However, the bottleneck in predicting subcellular protein locations of SRS cell imaging lies in modeling complicated relationships concealed beneath the original cell imaging data owing to the spectral overlap information from different protein molecules. In this work, a multiple parallel fusion network, MPFnetwork, is proposed to study the subcellular locations from SRS images. This model used a multiple parallel fusion model to construct feature representations and combined multiple nonlinear decomposing algorithms as the automated subcellular detection method. Our experimental results showed that the MPFnetwork could achieve over 0.93 dice correlation between estimated and true fractions on SRS lung cancer cell datasets. In addition, we applied the MPFnetwork method to cell images for label-free prediction of several different subcellular components simultaneously, rather than using several fluorescent labels. These results open up a new method for the time-resolved study of subcellular components in different cells, especially cancer cells.