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.
In principle, nonlinear multi-photon microscopy is straight forward to attain increased imaging resolution by √2 or more, in which the signal is only generated at the very center of the focal spot of laser. Label-free and nonlinear CARS and SRS microscopies are applicable to this spot reduction effect. However, their excitation laser wavelengths are limited to near infrared (NIR), partially because NIR is the wavelength only commercially available for typical femtosecond laser. Thus, the potential improvement in spatial resolution is completely compromised by longer wavelengths adopted for imaging. To fully utilize nonlinear advantage to defeat resolution limit, we reduced the wavelengths of our femtosecond lasers to visible region and demonstrate hyperspectral blue SRS microscopy with resolution about ~100 nm. Moreover, the electronic pre-resonance condition was reported to enhance sensitivity of SRS imaging as the absorption of NIR dyes match the laser frequencies. In our concept, we gained SRS sensitivity by actively tuning the wavelengths of pump and Stokes lasers to near resonant to electronic transition of endogenous biomolecules (e.g. DNA and proteins). Additionally, to achieve specificity to biomolecules, we developed single optical fiber based spectral focusing technology, and demonstrated high-resolution hyperspectral SRS imaging of intact tissues.
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.
Due to the subject nature of histopathology, there is a significant inter-observer discordance for the differentiation between low-risk prostate cancer (Gleason score ≤ 6), which can be left without treatment, and high-risk prostate cancer (Gleason score >6), which requires active treatment. Our previous study using Raman spectromicroscopy reveals that cholesteryl ester accumulation underlies human prostate cancer aggressiveness. However, Raman spectromicroscopy could only provide compositional information of certain lipid droplets of interest, which overlooked cell-to-cell variation and hindered translation to accurate automated diagnosis. Here, we demonstrated quantitative mapping of cholesteryl ester molar percentage in human prostate cancer tissues using hyperspectral stimulated Raman scattering microscopy that renders compositional information for every pixel in the image. Specifically, hundreds of SRS images at Raman shift between 2800~3000 cm-1 were taken, and multivariate curve resolution algorism was used to retrieve concentration images of lipid, lipofuscin, and protein. We found that the height ratio between the prominent cholesterol band at 2870 cm-1 and the CH2 stretching band at 2850 cm-1 was proportional to the molar percentage of cholesteryl ester present in the total lipids. Based on the calibration curve, we were able to quantitatively map cholesteryl ester level in intact prostate cancer tissues. Our data showed that not only the amount of cholesteryl ester-rich lipid droplets, but also the CE molar percentage, was significantly greater in prostate cancer tissues with Gleason score > 6 compared to the ones with Gleason score ≤ 6. Our study offers an opportunity towards more accurate prostate cancer diagnosis.
Most prostate cancers (PCa) are slowly growing, and only the aggressive ones require early diagnosis and effective treatment. The current standard for PCa diagnosis remains histopathology. Nonetheless, for the differentiation between Gleason score 6 (low-risk PCa), which can be left without treatment, and Gleason score 7 (high-risk PCa), which requires active treatment, the inter-observer discordance can be up to 40%. Our previous study reveals that cholesteryl ester (CE) accumulation induced by PI3K/AKT activation underlies human PCa aggressiveness. However, Raman spectromicroscopy used in this study could only provide compositional information of certain lipid droplets (LDs) selected by the observer, which overlooked cell-to-cell variation and hindered translation to accurate automated diagnosis. Here, we demonstrated quantitative mapping of CE level in human prostate tissues using hyperspectral stimulated Raman scattering (SRS) microscopy that renders compositional information for every pixel in the image. Specifically, hundreds of SRS images at Raman shift between 1620-1800 cm-1 were taken, and multivariate curve resolution algorism was used to retrieve concentration images of acyl C=C bond, sterol C=C bond, and ester C=O bond. Given that the ratio between images of sterol C=C and ester C=O (sterol C=C/C=O) is nonlinearly proportional to CE percentage out of total lipid, we were able to quantitatively map CE level. Our data showed that CE level was significantly greater in high Gleason grade compared to low Gleason grade, and could be a factor that significantly contributed to cancer recurrence. Our study provides an opportunity towards more accurate PCa diagnosis and prediction of aggressiveness.
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