QUANTITATIVE SPECTROSCOPIC IMAGING OF CANCER METABOLITES IN LIVE CELLS AND INTACT


Year of Award:
2013
Award Type:
R21
Project Number:
CA182608
RFA Number:
RFA-CA-13-001
Technology Track:
Molecular & Cellular Analysis Technologies
PI/Project Leader:
CHENG, JI-XIN
Other PI or Project Leader:
N/A
Institution:
PURDUE UNIVERSITY
While altered cell metabolism is an emerging hallmark of cancer, there is a crucial need of new tools for quantitation of metabolites. Though NMR spectroscopy, mass spectrometry, and Raman spectroscopy are widely used for molecular detection in tissue extracts or intact tissues, these tools do not tell the spatial locations of the analytes inside the cell. We address this unmet need via development of multiplex stimulated Raman scattering (SRS) microscopy to enable quantitative vibrational imaging of metabolites in live tumor cells and intact biopsies. The recently developed SRS microscopy allows high-speed, high-sensitivity imaging of single Raman bands in live cells. However, the single-frequency SRS imaging technique has limited capability because it cannot resolve molecular species that often have overlapped Raman bands. We propose to overcome this technical barrier via parallel detection of spectrally dispersed SRS signals enabled by a homebuilt tuned amplifier array. In a pilot study, we demonstrated multiplex SRS imaging of live pancreatic cancer cells with a pixel dwell time of 40 ¨s. In Aim 1, we will develop multiplex stimulated Raman loss (SRL) microscopy and multivariate analysis algorithm to enable quantitative vibrational imaging of lipid metabolites in live cells. In Aim 2, we will develop epi-detected multiplex SRL microscopy to enable high-speed, large-area spectroscopic imaging of tumor biopsies. By accomplishment of the two aims, we will generate a high- sensitivity, high-speed, spectral imaging platform for molecular analysis of live cells with sub-micron spatial resolution. This platform will permit label-free visualization of metabolic conversion in live cancr cells, which is not possible with proteomics tools. Such capability is critical for mechanistic understanding of cancer metabolism and precise evaluation of drugs targeting cancer metabolism. This platform will also permit large- area mapping of intact tumor biopsies and offer information about metabolic biomarkers (e.g. cholesteryl ester) that are indicative of cancer aggressiveness.