In vivo Imaging using Surface Enhanced Spatially Offset Raman Spectroscopy (SESORS): Balancing Sampling Frequency to Improve Overall Image Acquisition Institutional Repository Document uri icon


  • AbstractBackground and RationaleIn the field of optical imaging, the ability to image tumors at depth with high selectivity and specificity remains a challenge. Surface enhanced resonance Raman scattering (SERRS) nanoparticles (NPs) can be employed as image contrast agents to specifically target cellsin vivo, however, this technique typically requires time-intensive point-by-point acquisition of Raman spectra, thus hindering the real-time image acquisition desired for clinical applications. Moreover, traditional approaches involving Raman spectroscopy are limited in their inability to probe through tissue depths of more than a few millimeters. Here, we combine the use of spatially offset Raman spectroscopy (SORS) with that of SERRS in a technique known as surface enhanced spatially offset resonance Raman spectroscopy (SESORRS) to image deep-seated tumorsin vivo. Additionally, by accounting for the laser spot size, we report an experimental SESORRS approach for detecting both the bulk tumor, subsequent delineation of tumor margins at high speed, and the identification of a deeper secondary region of interest with fewer measurements than are typically applied.MethodsTo enhance light collection efficiency, four modifications were made to a previously described custom-built SORS system. Specifically, the following parameters were increased: (i) the numerical aperture (NA) of the lens, from 0.2 to 0.34; (ii) the working distance of the probe, from 9 mm to 40 mm; (iii) the NA of the fiber, from 0.2 to 0.34; and (iv) the fiber diameter, from 100 m to 400 m. To calculate the sampling frequency, which refers to the number of data point spectra obtained for each image, we considered the laser spot size of the elliptical beam (6 4 mm). Using SERRS contrast agents, we performedin vivoSESORRS imaging on a GL261-Luc mouse model of glioblastoma at four distinct sampling frequencies: par-sampling frequency (12 data points collected), and over-frequency sampling by factors of 2 (35 data points collected), 5 (176 data points collected), and 10 (651 data points collected).ResultsIn comparison to the previously reported SORS system, the modified SORS instrument showed a 300% improvement in signal-to-noise ratios (SNR). Glioblastomas were imagedin vivousing SESORRS in mice (n = 3) and tumors were confirmed using MRI and histopathology. The results demonstrate the ability to acquire distinct Raman spectra from deep-seated glioblastomas in mice through the skull using a low power density (6.5 mW/mm2) and 30-times shorter integration times than a previous report (0.5 s versus 15 s). The ability to map the whole head of the mouse and determine a specific region of interest using as few as 12 spectra (6 second total acquisition time) is achieved. Subsequent use of a higher sampling frequency demonstrates it is possible to delineate the tumor margins in the region of interest with greater certainty. In addition, SESORRS images indicate the emergence of a secondary tumor region deeper within the brain in agreement with MRI and H&E staining.ConclusionIn comparison to traditional Raman imaging approaches, this approach enables improvements in the rapid detection of deep-seated tumorsin vivothrough depths of several millimeters due to improvements in SNR, spectral resolution, and depth acquisition. This approach offers an opportunity to navigate larger areas of tissues in shorter time frames than previously reported, identify regions of interest, and then image such area with greater resolution using a higher sampling frequency. Moreover, using a SESORRS approach, we demonstrate that it is possible to detect secondary, deeper-seated lesions through the intact skull.

author list (cited authors)

  • Nicolson, F., Andreiuk, B., Lee, E., ODonnell, B., Whitley, A., Riepl, N., ... Haigis, K. M.

complete list of authors

  • Nicolson, Fay||Andreiuk, Bohdan||Lee, Eunah||O’Donnell, Bridget||Whitley, Andrew||Riepl, Nicole||Burkhart, Deborah||Cameron, Amy||Protti, Andrea||Rudder, Scott||Yang, Jiang||Mabbott, Samuel||Haigis, Kevin M

Book Title

  • bioRxiv

publication date

  • September 2023