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Spatially Offset and Transmission Raman Spectroscopy for Determination of Depth of Inclusion in Turbid Matrix.

ABSTRACT: We propose an approach for the prediction of the depth of a single buried object within a turbid medium combining spatially offset Raman spectroscopy (SORS) and transmission Raman spectroscopy (TRS) and relying on differential attenuation of individual Raman bands brought about by the spectral variation of matrix absorption (and scattering). The relative degree of the Raman band changes is directly related to the path length of Raman photons traveling through the medium, thereby encoding the information on the depth of the object within the matrix. Through a calibration procedure with root mean square error of calibration (RMSEC) = 3.4%, it was possible to predict the depth of a paracetamol (acetaminophen) inclusion within a turbid matrix consisting of polyethylene (PE) by monitoring the relative intensity of two Raman bands of paracetamol exhibiting differential absorption by the matrix. The approach was shown to be largely insensitive to variations of the amount of the inclusion (paracetamol) and to the overall thickness of the turbid matrix (PE) with a root mean square error of prediction (RMSEP) maintained below 10% for the tested cases. This represents a major advantage over previously demonstrated comparable depth determination Raman approaches (with the exception of full Raman tomography requiring complex mathematical reconstruction algorithms). The obtained experimental data validate the proposed approach as an effective tool for the noninvasive determination of the depth of buried objects in turbid media with potential applications including determining noninvasively the depth of a lesion in cancer diagnosis in vivo.

SUBMITTER: Mosca S

PROVIDER: S-EPMC7006960 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:A fundamental question crucial to surface-enhanced spatially offset Raman spectroscopy (SESORS) imaging and implementing it in a clinical setting for in vivo diagnostic purposes is whether a SESORS image can be used to determine the exact location of an object within tissue? To address this question, multiple experimental factors pertaining to the optical setup in imaging experiments using an in-house-built point-collection-based spatially offset Raman spectroscopy (SORS) system were investigated to determine those critical to the three-dimensional (3D) positioning capability of SESORS. Here, we report the effects of the spatial offset magnitude and geometry on locating nanoparticles (NPs) mixed with silica powder as an imaging target through tissue and outline experimental techniques to allow for the correct interpretation of SESORS images to ascertain the correct location of NPs in the two-dimensional x, y-imaging plane at depth. More specifically, the effect of "linear offset-induced image drag" is presented, which refers to a spatial distortion in SESORS images caused by the magnitude and direction of the linear offset and highlight the need for an annular SORS collection geometry during imaging to neutralize these asymmetric effects. Additionally, building on these principles, the concept of "ratiometric SESORS imaging" is introduced for the location of buried inclusions in three dimensions. Together these principles are vital in developing a methodology for the location of surface-enhanced Raman scattering-active inclusions in three dimensions. This approach utilizes the relationship between the magnitude of the spatial offset, the probed depth, and ratiometric analysis of the NP and tissue Raman intensities to ultimately image and spatially discriminate between two distinct NP flavors buried at different depths within a 3D model for the first time. This research demonstrates how to accurately identify multiple objects at depth in tissue and their location using SESORS which addresses a key capability in moving SESORS closer to use in biomedical applications.

Project description:Rationale: The goal of imaging tumors at depth with high sensitivity and specificity represents a significant challenge in the field of biomedical optical imaging. 'Surface enhanced Raman scattering' (SERS) nanoparticles (NPs) have been employed as image contrast agents and can be used to specifically target cells in vivo. By tracking their unique "fingerprint" spectra, it becomes possible to determine their precise location. However, while the detection of SERS NPs is very sensitive and specific, conventional Raman spectroscopy imaging devices are limited in their inability to probe through tissue depths of more than a few millimetres, due to scattering and absorption of photons by biological tissues. Here, we combine the use of "Spatially Offset Raman spectroscopy" (SORS) with that of "surface-enhanced resonance Raman spectroscopy" (SERRS) in a technique known as "surface enhanced spatially offset resonance Raman spectroscopy" (SESO(R)RS) to image deep-seated glioblastoma multiforme (GBM) tumors in vivo in mice through the intact skull. Methods: A SORS imaging system was built in-house. Proof of concept SORS imaging was achieved using a PTFE-skull-tissue phantom. Imaging of GBMs in the RCAS-PDGF/N-tva transgenic mouse model was achieved through the use of gold nanostars functionalized with a resonant Raman reporter to create SERRS nanostars. These were then encapsulated in a thin silica shell and functionalized with a cyclic-RGDyK peptide to yield integrin-targeting SERRS nanostars. Non-invasive in vivo SORS image acquisition of the integrin-targeted nanostars was then performed in living mice under general anesthesia. Conventional non-SORS imaging was used as a direct comparison. Results: Using a low power density laser, GBMs were imaged via SESORRS in mice (n = 5) and confirmed using MRI and histopathology. The results demonstrate that via utilization of the SORS approach, it is possible to acquire clear and distinct Raman spectra from deep-seated GBMs in mice in vivo through the skull. SESORRS images generated using classical least squares outlined the tumors with high precision as confirmed via MRI and histology. Unlike SESORRS, conventional Raman imaging of the same areas did not provide a clear delineation of the tumor. Conclusion: To the best of our knowledge this is the first report of in vivo SESO(R)RS imaging. In a relevant brain tumor mouse model we demonstrate that this technique can overcome the limitations of conventional Raman imaging with regards to penetration depth. This work therefore represents a significant step forward in the potential clinical translation of SERRS nanoparticles for high precision cancer imaging.

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Spatially Offset and Transmission Raman Spectroscopy for Determination of Depth of Inclusion in Turbid Matrix.

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