Current Hyperspectral stimulated Raman scattering (hsSRS) data analysis methods face challenges when it comes to rapidly and reliably quantifying different lipid subtypes, and cannot fully leverage the information in hsSRS data. Here, we present a rapid and reliable quantitative algorithm for quantitative analysis that fully extracts chemical information by using adaptive selection of Lorentzian basis functions to fit the spectra in hsSRS data in bulk. We demonstrated that, by utilizing the ratio relationships between fitted bands, quantitative comparisons of specific lipid subtypes can be achieved. Moreover, we applied our method for the quantitative analysis of lipid composition in lipid droplets based on hsSRS data of liver cancer tissues and confirmed our method has a better fitting effect and a faster solving speed compared to MCR. This suggests that our method has the potential for great utility in the quantitative analysis of hsSRS imaging data for biomedical specimens.
The number of neuronal cells is fundamentally important for brain functions. However, it can be difficult to obtain the accurate number of neuronal cells in large-scale brain imaging, which is nearly inevitable with traditional image segmentation techniques due to the low contrast and noisy background. Here, we introduce a Docker-based deep convolutional neural network (DDeep3M) for better counting neurons in the stimulated Raman scattering (SRS) microscopy images. To reconcile the memory limit of computational resource, a high-resolution 2D SRS image of whole coronal slice of mouse brain is divided into multiple patch images. Each patch image is then fed into the DDeep3M and predicted as a probability map. A higher contrast image targeting neurons (i.e. the predicted image) can be acquired by stitching the patches of probability map together. With this routine segmentation method applied in both raw SRS image and the predicted image, the DDeep3M achieves the accuracy of over 0.96 for cell counting which is much better than the result of traditional segmentation methods. Compared with the U-Net, which is one of the most popular deep learning networks for medical image segmentation, DDeep3M demonstrates a better result when handling such large-scale image. Thus, DDeep3M can be really helpful for large-scale cell counting in brain research.
Altered lipid metabolism is increasingly recognized as a signature of cancer cells. Enabled by label-free spectroscopic imaging, we performed quantitative analysis of lipogenesis at single-cell level in human clear cell renal cell carcinoma (ccRCC), which accounts for about 90% kidney cancers. Our hyperspectral stimulated Raman scattering (SRS) imaging data revealed an aberrant accumulation of lipid droplets in human clear cell renal cell carcinoma (ccRCC), but no detectable lipid droplets in normal or benign kidney tissues. We also found that such lipid accumulation was significantly higher in low grade (Furhman Grade≤2) ccRCC compared that in high grade (Furhman Grade≥3) ccRCC, and was correlated well with the prognosis of ccRCC. Moreover, cholesteryl ester is the dominant form of lipids accumulated in ccRCC. Besides, the unsaturation level of lipids was significantly higher in high grade ccRCC compared to low grade ccRCC. Furthermore, depletion of cholesteryl ester storage significantly reduced cancer proliferation, impaired cancer invasion capability, and suppressed tumor growth and metastasis in mouse xenograft and orthotopic models, with negligible toxicity. These findings herald the potential of using lipid accumulation as a marker for diagnosis of human ccRCC and open a new way of treating aggressive human ccRCC by targeting the altered lipid metabolism.
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