Stimulated Raman scattering (SRS) microscopy has been used for rapid label-free imaging of various biomolecules and drugs in living cells and tissues (Science, doi:10.1126/science.aaa8870). Our recent work has demonstrated that lipid and protein mapping of cancer tissue renders pathology-like images, providing essential histopathological information with subcellular resolution of the entire specimen (Cancer Research, doi: 10.1158/0008-5472.CAN-16-027). We have also established the first SRS imaging Atlas of human brain tumors (Harvard Dataverse, doi: (doi:10.7910/DVN/EZW4EK). SRS imaging of tissue could provide invaluable information for cancer diagnosis and surgical guidance in two aspects: rapid surgical pathology and quantitative biomolecular characterization. In this work, we present the use of SRS microscopy for characterization of a few essential biomolecules in breast cancer. Human breast cancer tissue specimens at the tumor core, tumor margin and normal area (5 cm away from the tumor) from surgical cases will be imaged with SRS at multiple Raman shifts, including the peaks for lipid, protein, blood (absorption), collagen, microcalcification (calcium phosphates and calcium oxalate) and carotenoids. Most of these Raman shifts have relatively strong Raman cross sections, which ensures high-quality and fast imaging. This proof-of-principle study is sought to demonstrate the feasibility and potential of SRS imaging for ambient diagnosis and surgical guidance of breast cancer.
Desorption electrospray ionization mass spectrometry (DESI-MS) provides a highly sensitive imaging technique for differentiating normal and cancerous tissue at the molecular level. This can be very useful, especially under intra-operative conditions where the surgeon has to make crucial decision about the tumor boundary. In such situations, the time it takes for imaging and data analysis becomes a critical factor. Therefore, in this work we utilize compressive sensing to perform the sparse sampling of the tissue, which halves the scanning time. Furthermore, sparse feature selection is performed, which not only reduces the dimension of data from about 104 to less than 50, and thus significantly shortens the analysis time. This procedure also identifies biochemically important molecules for further pathological analysis. The methods are validated on brain and breast tumor data sets.
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