Chemical imaging for biological systems: techniques, AI-driven processing, and applications

Abstract

Visualizing the chemical compositions of biological samples is pivotal to advancing biological sciences, with the past two decades witnessing the emergence of innovative chemical imaging platforms such as single-molecule imaging, coherent Raman scattering microscopy, transient absorption microscopy, photothermal microscopy, ambient ionization mass spectrometry, electrochemical microscopy, and advanced chemical probes. These technologies have enabled significant breakthroughs in diagnosing pathological transitions, designing targeted therapies, and understanding drug resistance mechanisms. Recent advancements in resolution, contrast, sensitivity, and speed have transformed the field, with techniques like fluorescence, infrared absorption, and Raman scattering being widely applied across diverse biological domains. This review provides a comprehensive overview of the evolution and current state of chemical imaging technologies, coupled with systematic analyses of data processing workflows, including pre-processing, machine learning-assisted pattern extraction, and neural network-based predictions. Artificial intelligence (AI) and machine learning-assisted imaging are transforming chemical imaging through key advancements such as improved resolution and sensitivity via noise reduction techniques, enhanced data analysis (e.g., spectral unmixing, pattern recognition), automated feature extraction using neural networks, real-time processing via high-performance cluster, and data fusion across optical platforms. These innovations are significantly advancing both current applications and the future development of chemical imaging techniques in biomedical research. However, several critical challenges remain, including the scarcity of high-quality training datasets, limited generalizability across different instruments and experimental conditions, high computational costs, challenges in output interpretability and trust, and the lack of standardized validation protocols across different approaches. Looking ahead, the integration of bioimaging into cell biology, lipid research, tumor studies, microbiology, neurobiology, and developmental biology is anticipated to expand its impact, aided by interdisciplinary expertise in biochemistry, physics, and optical engineering. These developments promise unprecedented resolution and speed, facilitating high-speed, high-resolution imaging of living systems, with applications leading to discoveries such as biomarkers for cancer aggressiveness and drug resistance. Moreover, the miniaturization and commercialization of imaging platforms are broadening accessibility, enabling on-site clinical investigations and in vivo measurements, underscoring the transformative potential of chemical imaging in advancing biological science and medical research.

Figure 1:. Timeline of the technical advancements in imaging for biological systems.

Reproduced from reference [12] with permission from [Springer Nature], copyright [2019]. Reproduced from reference [73] with permission from [Springer Nature], copyright [2019].

Figure 2:. Workflow of AI-assisted bioimaging and diagnostics.

Reproduced from reference [73] with permission from [Springer Nature], copyright [2019]. Reproduced from reference [108] with permission from [American Chemical Society], copyright [2013]. Reproduced from reference [132] with permission from [American Association for the Advancement of Science], copyright [2023].

Figure 3:. Imaging techniques.

(a) Fluorescence microscopy. (b) Super-resolution microscopy. (c) Raman microscopy. Reproduced from reference [12] with permission from [Springer Nature], copyright [2019]. Reproduced from reference [92] with permission from [Springer Nature], copyright [2019].

Figure 4:. Summary of data-processing methods in imaging.

(a) Denoise: Comparison of Raman spectra with Savitzky-Golay filtering and detrending under low laser excitation conditions. Reproduced from reference [98] with permission from [Elsevier], copyright [2021]. (b) Spike removal: Raman spectra from a cellular system. Reproduced from reference [92] with permission from [Springer Nature], copyright [2019]. (c) Normalization: Normalized Raman spectra with different PQT-12 polymer layers. Reproduced from reference [104] with permission from [AIP Publishing], copyright [2014]. (d) PCA: Score plot of IMS data from normal and the tumor tissue sections, showing complete and reduced data sets in blue and red, respectively. Reproduced from reference [108] with permission from [American Chemical Society], copyright [2013]. (e) VCA: Retrieved VCA image with three endmembers from atherosclerotic rabbit aorta tissue samples. Reproduced from reference [116] with permission from [WILEY], copyright [2015]. (f) Pipeline for Identifying Cancer Driver Modules by Graph Embedding and Hierarchical Clustering (ICDM-GEHC). Reproduced from reference [130] with permission from [Springer Nature], copyright [2024]. (g) Regression: Schematic illustration of pixel-wise LASSO spectral unmixing for chemical mapping generation. Reproduced from reference [132] with permission from [American Association for the Advancement of Science], copyright [2023]. (h) Architecture of a hybrid HCNN-KNN Model for age estimation in orthopantomography. Reproduced from reference [144] with permission from [Frontiers], copyright [2022]. (i) t-SNE visualization of top-ranked analogue of Palbociclib and analogues of Ribociclib. Reproduced from reference [152] with permission from [MIT Press, Microtome Publishing, JMLR, Inc.], copyright [2008]. (j) Neural network architecture with multiple hidden layers, each adopting potentially different activation functions. Reproduced from reference [155] with permission from [Taylor & Francis Online], copyright [2024]. (k) Improved CNN architecture for recognizing and classifying biological images. Reproduced from reference [159] with permission from [Innovative Information Science & Technology Research Group], copyright [2025]. (l) Optimized deep neural network architecture for gene expression analysis. Reproduced from reference [163] with permission from [American Chemical Society], copyright [2022].

Figure 5:. Imaging for Biological Applications.

(a) Cell division visualized by fluorescence microscopy. Reproduced from reference [175] with permission from [Springer Nature], copyright [2014]. (b) Lipid organelles recorded with fluorescence microscopy. Reproduced from reference [180] with permission from [Springer Nature], copyright [2020]. (c) Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging. Reproduced from reference [190] with permission from [Springer Nature], copyright [2009]. (d) Visualization of individual type III protein secretion machines in live bacteria. Reproduced from reference [201] with permission from [Proceedings of the National Academy of Sciences of the United States of America], copyright [2017]. (e) Whole-brain neuronal activity of a larval zebrafish recorded with a light-sheet microscope. Reproduced from reference [209] with permission from [Springer Nature], copyright [2013]. (f) Photoacoustic imaging of thrombosis via fibrin-specific homopolymer nanoparticles. Reproduced from reference [218] with permission from [Springer Nature], copyright [2023].