ClearAIM: dynamic quantitative label-free monitoring of tissue optical clearing

Achieving cellular-resolution insights into an organ or whole-body architecture is a cornerstone of modern biology.Recent advancements in tissue clearing techniques have revolutionized the visualization of complex structures, enhancing tissue transparency by mitigating light scattering caused by refractive index mismatches and fast-changing scattering element distribution.However, the field remains constrained by predominantly qualitative assessments of clearing methods, with systematic, quantitative approaches being scarce.Here, we present the ClearAIM method for real-time quantitative monitoring of the tissue clearing process.It leverages a tailored deep learning-based segmentation algorithm with a bespoke frame-to-frame scheme to achieve robust, precise, and automated analysis.Demonstrated using mouse brain slices (0.5 and 1 mm thick) and the CUBIC method, our universal system enables (1) precise quantification of dynamic transparency levels, (2) real-time monitoring of morphological changes via automated analysis, and (3) optimization of clearing timelines to balance increased transparency with structural information preservation.The presented system enables rapid, user-friendly measurements of tissue transparency and shape changes without the need for advanced instrumentation.These features facilitate objective comparisons of the effectiveness of tissue clearing techniques for specific organs, relying on quantifiable values rather than predominantly empirical observations.Our method promotes increased diagnostic values and consistency of the cleared samples, ensuring the repeatability and reproducibility of biomedical tests.