a-flexible-chamber-for-time-lapse-live-cell-imaging-with-stimulated

A Flexible Chamber for Time-Lapse Live-Cell Imaging with Stimulated Raman Scattering Microscopy

Stimulated Raman scattering (SRS) microscopy is a label-free chemical imaging technology. Live-cell imaging with SRS has been demonstrated for many ...

State University of New York

Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor. The effectiveness of traditional therapies for GBM is limited and therefore new therapies are highly desired. Previous studies show that lipid metabolism reprogramming may be a potential therapeutic target in GBM. This study aims to evaluate the therapeutic potential of free fatty acid-induced lipotoxicity for the suppression of glioma growth. U87 glioma cells are treated with three fatty acids (FAs): palmitic acid (PA), oleic acid (OA), and eicosapentaenoic acid (EPA). Uptake of the FAs and formation of lipid droplets (LDs) are imaged and quantified using a lab-built stimulated Raman scattering (SRS) microscope. Our results show that a supply of 200 µM PA, OA, and EPA leads to efficient LDs accumulation in glioma cells. We find that inhibition of triglycerides (TAGs) synthesis depletes LDs and enhances lipotoxicity, which is evidenced by the reduced cell proliferation rates. In particular, our results suggest that EPA treatment combined with depletion of LDs significantly reduces the survival rate of glioma cells by more than 50%, indicating the therapeutic potential of this approach. Future work will focus on understanding the metabolic mechanism of EPA-induced lipotoxicity to further enhance its anticancer effects.

Assessing fatty acid-induced lipotoxicity and its therapeutic potential in glioblastoma using stimulated Raman microscopy.

In this paper, we propose an automatic cell counting framework for stimulated Raman scattering (SRS) images, which can assist tumor tissue characteristic analysis, cancer diagnosis, and surgery planning processes. SRS microscopy has promoted tumor diagnosis and surgery by mapping lipids and proteins from fresh specimens and conducting a fast disclose of fundamental diagnostic hallmarks of tumors with a high resolution. However, cell counting from label-free SRS images has been challenging due to the limited contrast of cells and tissue, along with the heterogeneity of tissue morphology and biochemical compositions. To this end, a deep learning-based cell counting scheme is proposed by modifying and applying U-Net, an effective medical image semantic segmentation model that uses a small number of training samples. The distance transform and watershed segmentation algorithms are also implemented to yield the cell instance segmentation and cell counting results. By performing cell counting on SRS images of real human brain tumor specimens, promising cell counting results are obtained with > 98% of area under the curve (AUC) and R = 0.97 in terms of cell counting correlation between SRS and histological images with hematoxylin and eosin (H&E) staining. The proposed cell counting scheme illustrates the possibility and potential of performing cell counting automatically in near real time and encourages the study of applying deep learning techniques in biomedical and pathological image analyses.

Article	Year
Assessing fatty acid-induced lipotoxicity and its therapeutic potential in glioblastoma using stimulated Raman microscopy. Scientific reports\| PubMed ID: 33795756	2021
Automatic cell counting from stimulated Raman imaging using deep learning. PloS one\| PubMed ID: 34288972	2021

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