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Autofluorescence (AF) poses challenges for detecting proteins of interest in situ when employing immunofluorescence (IF) microscopy. This interference is particularly pronounced in strongly autofluorescent tissues such as myocardium, where tissue AF can be comparable to IF. Although various histochemical methods have been developed to achieve effective AF suppression in different types of tissue, their applications on myocardial  samples have not been well validated. Due to inconsistency across different autofluorescent structures in sometypes of tissue, it is unclear if these methods can effectively suppress AF across all autofluorescent structures within the myocardium. Here, we quantitatively evaluated the performance of several commonly used quenching treatments on formaldehyde-fixed myocardial samples, including 0.3 M glycine, 0.3% Sudan Black B (SBB), 0.1% and 1% sodium borohydride (NaBH4), TrueVIEW® and TrueBlack®. We further assessed their quenching performance by employing the pre-treatment and post-treatment protocols, designed to cover two common IF staining scenarios where buffers contained detergents or not. The results suggest that SBB and TrueBlack® outperform other reagents in AF suppression on formaldehyde-fixed myocardial samples in both protocols. Furthermore, we inspected the quenching performance of SBB and TrueBlack® on major autofluorescent myocardial structures and evaluated their influence on IF imaging. The results suggest that SBB outperforms TrueBlack® in quenching major autofluorescent structures, while TrueBlack® excels in preserving IF labeling signal. Surprisingly, we found the treatment of NaBH4 increased AF signal and enhanced the AF contrast of major autofluorescent structures. This finding suggests that NaBH4 has the potential to act as an AF enhancer and may facilitate the interpretation of myocardial structures without the need for counterstaining. 

Chondrocyte viability is a crucial factor in evaluating cartilage health. Most cell viability assays rely on dyes and are not applicable forin vivoor longitudinal studies. We previously demonstrated that two-photon excited autofluorescence and second harmonic generation microscopy provided high-resolution images of cells and collagen structure; those images allowed us to distinguish live from dead chondrocytes by visual assessment or by the normalized autofluorescence ratio. However, both methods require human involvement and have low throughputs. Methods for automated cell-based image processing can improve throughput. Conventional image processing algorithms do not perform well on autofluorescence images acquired by nonlinear microscopes due to low image contrast. In this study, we compared conventional, machine learning, and deep learning methods in chondrocyte segmentation and classification. We demonstrated that deep learning significantly improved the outcome of the chondrocyte segmentation and classification. With appropriate training, the deep learning method can achieve 90% accuracy in chondrocyte viability measurement. The significance of this work is that automated imaging analysis is possible and should not become a major hurdle for the use of nonlinear optical imaging methods in biological or clinical studies.